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Efficient Parameterization for Knowledge Graph Embedding Using Hierarchical Attention Network

Computers, Materials & Continua 2025年第3期82卷 4287-4300页

作者： Zhen-Yu Chen Feng-Chi Liu Xin Wang Cheng-Hsiung Lee Ching-Sheng Lin Master Program of Digital Innovation Tunghai UniversityTaichung40704Taiwan Department of Statistics Feng Chia UniversityTaichung40724Taiwan College of Integrated Health Sciences and the AI Plus Institute The University at AlbanyState University of New York(SUNY)AlbanyNY 12222USA

In the domain of knowledge graph embedding,conventional approaches typically transform entities and relations into continuous vector ***,parameter efficiency becomes increasingly crucial when dealing with large-scale knowledge graphs that contain vast numbers of entities and *** particular,resource-intensive embeddings often lead to increased computational costs,and may limit scalability and adaptability in practical environ-ments,such as in low-resource settings or real-world *** paper explores an approach to knowledge graph representation learning that leverages small,reserved entities and relation sets for parameter-efficient *** introduce a hierarchical attention network designed to refine and maximize the representational quality of embeddings by selectively focusing on these reserved sets,thereby reducing model *** assessments validate that our model achieves high performance on the benchmark dataset with fewer parameters and smaller embedding *** ablation studies further highlight the impact and contribution of each component in the proposed hierarchical attention structure.

关键词： Knowledge graph embedding parameter efficiency representation learning reserved entity and relation sets hierarchical attention network

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Survival Information System Using ReliefF Feature Selection and Backpropagation in Hepatocellular Carcinoma Disease 24

Survival Information System Using ReliefF Feature Selection ...

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24th International Seminar on Intelligent Technology and Its Applications, ISITIA 2023

作者： Wulandari, Umi Meganinditya Warsito, Budi Farikin, Farikhin Diponegoro University Master Program of Information Systems Semarang Indonesia Diponegoro University Department of Statistics Semarang Indonesia Diponegoro University Department of Mathematichs Semarang Indonesia

ISBN: (纸本)9798350313956

This study was conducted to classify patients with hepatocellular carcinoma by dividing the dataset features. The purpose of this study was to select the features in medical records with the influential features on hepatocellular carcinoma. In this paper, the proposed model is the use of ReliefF at the feature selection stage and Backpropagation at the classification stage. At this feature selection stage, there are two steps, namely weight calculation and feature reduction process. In the feature weight calculation step, each feature is given a weight and the resulting features will be processed in the feature reduction process. The results of the feature weight calculation will produce a ranking from the highest value to the lowest value which will then be reduced by the feature ranking. The best features that have been produced will be used as input for the second stage, namely the classification stage using Backpropagation. The results showed that the 10 features used were the best features out of 39 features. The best accuracy is produced by the ReliefF+BPNN method of 85%. The comparison results show that the ReliefF feature selection method has the best success rate among all. The results of this study indicate that the proposed method is successful. © 2023 IEEE.

关键词： Classification (of information)

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Spatial and Time Series Modelling for the Groundwater Level of Peatlands in Riau and Central Kalimantan, Indonesia 1

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4th International Conference on Mathematics, statistics, and Computing Technology, ICMSCT 2023

作者： Mukhaiyar, Utriweni Mahdiyasa, Adilan Widyawan Prastoro, Tarasinta Suherlan, Bagas Caesar Pasaribu, Udjianna Sekteria Indratno, Sapto Wahyu Statistics Research Division Faculty of Mathematics and Natural Sciences Institut Teknologi Bandung Bandung Indonesia Master Program in Actuarial Science Faculty of Mathematics and Natural Sciences Institut Teknologi Bandung Bandung Indonesia

ISBN: (数字)9789819734504

ISBN: (纸本)9789819734498

Peatlands serve as vital carbon reservoirs, exhibiting two stability conditions. In a stable state, they absorb CO2 with slow decomposition. In contrast, an unstable state causes faster decomposition, leading to increased carbon emissions and a higher risk of peatland fires. Studies have found that carbon emissions positively correlate with lower water table depths, resulting in a higher emission rate. Considering peatlands’ behaviour, an autoregressive integrated moving average (ARIMA) model was used to predict the groundwater level in order to anticipate the risk of the unstable condition. The data taken was in Indragiri Hilir, Riau, and Pulang Pisau, Central Kalimantan, from 27 February 2021 to 18 March 2023. Furthermore, the Ordinary Kriging (OK) was used to estimate the groundwater level in the unobserved locations. Thus, the potential risk map of peatlands could be obtained. The forecasts showed that the groundwater level area decreased from March to May 2023, in line with the season change. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Carbon sequestration

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FinGAT: Financial Graph Attention Networks for Recommending Top-K Profitable Stocks

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IEEE Transactions on Knowledge and Data Engineering 2023年第1期35卷 469-481页

作者： Hsu, Yi-Ling Tsai, Yu-Che Li, Cheng-Te National Taiwan University Master Program in Statistics Taipei10617 Taiwan National Taiwan University Department of Computer Science and Information Engineering Taipei10617 Taiwan National Cheng Kung University Institute of Data Science Tainan701 Taiwan

Financial technology (FinTech) has drawn much attention among investors and companies. While conventional stock analysis in FinTech targets at predicting stock prices, less effort is made for profitable stock recommendation. Besides, in existing approaches on modeling time series of stock prices, the relationships among stocks and sectors (i.e., categories of stocks) are either neglected or pre-defined. Ignoring stock relationships will miss the information shared between stocks while using pre-defined relationships cannot depict the latent interactions or influence of stock prices between stocks. In this work, we aim at recommending the top-K profitable stocks in terms of return ratio using time series of stock prices and sector information. We propose a novel deep learning-based model, Financial Graph Attention Networks (FinGAT), to tackle the task under the setting that no pre-defined relationships between stocks are given. The idea of FinGAT is three-fold. First, we devise a hierarchical learning component to learn short-term and long-term sequential patterns from stock time series. Second, a fully-connected graph between stocks and a fully-connected graph between sectors are constructed, along with graph attention networks, to learn the latent interactions among stocks and sectors. Third, a multi-task objective is devised to jointly recommend the profitable stocks and predict the stock movement. Experiments conducted on Taiwan Stock, S&P 500, and NASDAQ datasets exhibit remarkable recommendation performance of our FinGAT, comparing to state-of-the-art methods. © 1989-2012 IEEE.

关键词： Forecasting

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Bibliometric Analysis: Research Trends in Leaf Image Segmentation and Classification

Bibliometric Analysis: Research Trends in Leaf Image Segment...

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2024 International Conference on Artificial Intelligence and Mechatronics System, AIMS 2024

作者： Agustini, Eka Puji Gernowo, Rahmad Kurnianingsih Wibowo, Adi Warsito, Budi Doctoral Program of Information System School of Postgraduated Studies Diponegoro University Semarang Indonesia Doctoral Program of Information System Diponegoro University Semarang Indonesia Master of Applied Science Program Polytechnic Semarang Indonesia Diponegoro University Faculty of Science and Mathematics Department of Informatics Semarang Indonesia Faculty of Science and Mathematics Diponegoro Universitty Department of Statistics Semarang Indonesia

ISBN: (纸本)9798350350524

Exploring various phenomena and issues related to leaf images is paramount, particularly in segmentation and classification of such images. This study employs bibliometric analysis to delve into two overarching themes: The trends in publication and the evolution of publications, along with a keyword-based analysis. The research methodology unfolds in two stages: (1) data collection and (2) analysis. The dataset comprises 1,248 articles sourced from the Scopus database, covering the period from 1988 to 2023. The research findings unveil a noteworthy surge in publication trends, peaking at 231 documents in 2023. An in-depth examination of journal names demonstrates that studies on the segmentation and classification of leaf images are predominantly featured in computer science journals, exemplified by the publication of 589 documents. Furthermore, an analysis of frequently used keywords highlights 'Extraction' as the predominant term, employed a total of 364 times. This underscores that the research focus on leaf image segmentation and classification presents ample opportunities for researchers to delve more profoundly into the subject. © 2024 IEEE.

关键词： Image segmentation

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Static and dynamic panel models: Which is better? (Case study: Poverty data in Indonesia 2012-2019) 1

Static and dynamic panel models: Which is better? (Case stud...

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1st Universitas Lampung International Conference on Science, Technology and Environment, ULICOSTE 2020

作者： Sihombing, P.R. Arsani, A.M. Departemen of Statistics IPB University Bogor Indonesia Applied Economics Master Program Padjadjaran University Bandung Indonesia Statistics Indonesia Indonesia

Indonesia's poverty rate continues to decline from year to year;however, the decline in the percentage of poor people is slowing down continuously, following the law of diminishing returns. Many factors influence poverty, including HDI, Gini ratio, and the open unemployment rate. This study model static and dynamic panel data on Indonesia's poverty cases in 2012-2019 to explain the relationship between HDI, Gini ratio, and the unemployment rate to poverty. It also compares poverty modeling with static and dynamic panel data regression. The results show that the Gini ratio has a significant and positive relationship to poverty. HDI has a significant and negative relationship to poverty. However, there is insufficient evidence to suggest that unemployment affects poverty. Using static panel data regression, the selected model is a fixed effect model but still experiences violations of the classic autocorrelation assumption. The dynamic panel data model gives better results than the static panel data regression model when viewed from the r-square value and the number of variables that significantly affect. From the results of this study, it can be implemented that an increase in economic growth without an increase in equity has no impact on poverty. So, it needs extra efforts by the government in economic equality for all regions of Indonesia. Also, there is a need for additional actions by the government and the community to improve education, health, purchasing power, and community competitiveness. © Published under licence by IOP Publishing Ltd.

关键词： Population statistics

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Primarily research on the mathematical problem-solving abilities of grade X Vocational High School students for Computer and Network Engineering Competencies 4

Primarily research on the mathematical problem-solving abili...

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4th International Conference on Mathematics and Mathematics Education, ICM2E 2020

作者： Oktari, A. Permana, D. Master Program Universitas Negeri Padang Padang City Indonesia Department of Statistics Universitas Negeri Padang Padang City Indonesia

Computer and network engineering are one of the skill competencies in Vocational high school. Graduates of this major are expected to have problem-solving abilities to be able to compete in the working world. Mathematics is a subject that can develop mathematical problemsolving abilities. This study aimed to see, review and describe the mathematical problem-solving abilities of students and draw conclusions about these things as the phenomena found and to review the conditions of the learning devices used and developed by the teacher. This research used descriptive quantitative research methods. The data was collected through interviews, observations, and giving tests. Data analysis techniques used descriptive techniques for the results of observation and interview data, and scoring rubrics for mathematical problem-solving abilities for test result data. The results showed that the students' mathematical problem-solving abilities were still not relatively low. Some of the factors that cause this are 1) the learning was still using the lecturing method so that learning became meaningless, 2) students were accustomed to copying and memorizing formulas given by the teacher without understanding them, 3) the learning tools used were the same for each skill program, 4) a mathematics learning approach had not led students to be actively involved in the learning process and had not guided students to construct their knowledge in improving mathematical problem-solving abilities. Based on these results, further action is needed in the form of research on the development of constructivism-based learning tools to improve the mathematical problem-solving abilities of students in the Vocational High School for Computer and Network Engineering Competencies. © Published under licence by IOP Publishing Ltd.

关键词： Students

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Gene-Environment Interactions and Gene–Gene Interactions on Two Biological Age Measures: Evidence from Taiwan Biobank Participants

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Advanced Biology 2024年第7期8卷 e2400149页

作者： Lin, Wan-Yu Institute of Health Data Analytics and Statistics College of Public Health National Taiwan University Taipei100 Taiwan Master of Public Health Degree Program College of Public Health National Taiwan University Taipei100 Taiwan

PhenoAge and BioAge are two commonly used biological age (BA) measures. The author here searched for gene-environment interactions (GxE) and gene-gene interactions (GxG) on PhenoAgeAccel (age-adjusted PhenoAge) and BioAgeAccel (age-adjusted BioAge) of 111,996 Taiwan Biobank (TWB) participants, including a discovery set of 86,536 TWB2 individuals and a replication set of 25,460 TWB1 individuals. Searching for variance quantitative trait loci (vQTLs) provides a convenient way to evaluate GxE and GxG. A total of 4 nearly independent (linkage disequilibrium measure r2 © 2024 The Authors. Advanced Biology published by Wiley-VCH GmbH.

关键词： Phylogenetic

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Survival Information System Using ReliefF Feature Selection and Backpropagation in Hepatocellular Carcinoma Disease

Survival Information System Using ReliefF Feature Selection ...

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International Seminar on Intelligent Technology and Its Applications (ISITIA)

作者： Umi Meganinditya Wulandari Budi Warsito Farikhin Farikin Master Program of Information Systems Diponegoro University Semarang Indonesia Department of Statistics Diponegoro University Semarang Indonesia Department of Mathematichs Diponegoro University Semarang Indonesia

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Clustering Analysis of a Spatiotemporal Dataset with a Novel Kernel Density Estimator

Clustering Analysis of a Spatiotemporal Dataset with a Novel...

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2023 International Conference on Machine Learning and Cybernetics, ICMLC 2023

作者： Yu, Jen-Chien Yang, Chun-Chieh Gilbert, John Reuben Liu, Rou-Jun Oyang, Yen-Jen Yang, Meng-Han National Kaohsiung University of Science and Technology Department of Computer Science and Information Engineering Kaohsiung City807618 Taiwan National Taiwan University Master Program in Statistics Taipei106216 Taiwan National Taiwan University Department of Computer Science & Information Engineering Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taiwan

ISBN: (纸本)9798350303780

A vast number of spatiotemporal datasets collected from a wide range of sources has motivated scientists to develop effective approaches to identify interesting patterns hidden in these datasets. In this respect, kernel density estimators, which belong to a class of non-parametric estimators in statistics, have been widely exploited in recent years. With this background, we have developed a novel kernel density estimator aiming to provide accurate analysis results. According to the evaluation with a real spatiotemporal dataset, which collected emergency medical service records in a county in the United States, the proposed kernel density estimator can approximate the probability density function significantly more accurately than a conventional kernel density estimator. Furthermore, we have exploited the proposed kernel density estimator to identify interesting patterns hidden in the real spatiotemporal dataset. © 2023 IEEE.

关键词： Probability density function

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