Search

This paper aims to classify the types of stress experienced by women undergoing infertility treatment in Japan and to clarify the causes of such stress. To achieve this, tweets posted by infertility patients were used to identify the causes of stress through a learning model, followed by dependency analysis. A system is proposed to extract causal relations using dependency analysis, where the dependents are the causes and the leaders are the effects. The study focuses on the stress experienced by infertility patients and limits the analysis to "family issues" and "work." Consequently, the incentives for the causes were clarified. [ABSTRACT FROM AUTHOR]

This paper explores the characteristics of a two-step procedure (dimension reduction and function approximation) in discrete choice modeling with high-dimension data. This study proposes the SS-GAM procedure which is an extension of the Super Sparse Principal Component Analysis (SSPCA) where the results are further processed with the Generalized Additive Model (GAM) in a classification problem. Moreover, the Orthogonal Sparse Principal Component Analysis with GAM (OS-GAM) is also proposed. For baseline comparison, the General Adaptive Sparse-PCA with GAM (GAS-GAM) is considered in this paper. The performance of these three sparse PCA methods are investigated with varied underlying correlation structure. In the simulation study, it is demonstrated that with varied degree of dimensionality, and levels of correlation structure, SS-GAM performed better compared to OS-GAM and GAS-GAM in terms of its predictive rate, on the average. It was observed that the OS-GAM performed best when data exhibits low correlation structure. However, with high correlation structure, OS-GAM and GAS-GAM obtained comparable result. Moreover, in terms of computational time, OS-GAM seems to be not affected by the increase of feature dimension. [ABSTRACT FROM AUTHOR]

The sustainability issue in the transportation sector brings the electric vehicle (EV) as a new promising solution for reducing carbon emissions. However, the EV adoption faces issues regarding range, recharging time, and high initial investment. To enhance the adoption, supporting infrastructures should be planned. Therefore, forecasting the growth of EV adoption becomes important to help industries and government in strategic decision making. This paper provides a literature review about forecasting methods in EV ownership, which includes studies from 2011 to March 2023. This will contribute to highlight the current methods and stimulating more developments of forecasting methods related to EV ownership. [ABSTRACT FROM AUTHOR]

As start-up companies may leverage information spreading through Instagram's hashtag-setting, algorithms in influence maximization can be encoded. The objective is to find a maximum impact which can activate users in spreading advertisement marketing campaign through social network. This paper contributes an influence analysis on Instagram and reproduces it into a hashtag network by transforming postings with hashtag interdependence. In finding optimal paths such network structure with influence probabilities, we employ Floyd-Warshall algorithm under uncertainty. The technique is simulated on Indonesian FnB unicorn company and demonstrated in numerical example. The result shows that the hashtag #promokopi may impact the most. [ABSTRACT FROM AUTHOR]

We introduce the combination of two techniques: Sparse Retrieval and Dense Retrieval, while experimenting with different training approaches to find the optimal method for the Vietnamese Legal Text Retrieval task. Moreover, the Question Answering task was only built on the open domain of UIT-ViQuAD but shown promising results on the in-domain legal dataset. Finally, we also mentioned the data augmentation of legal documents up to 3GB to train the Phobert language model, improve this backbone with Condenser, Cocondenser in this paper. Furthermore, these techniques can be utilized for other information retrieval assignments in languages with limited resources. [ABSTRACT FROM AUTHOR]

Hate Speech already been phenomenal expansion over the past decade. The paper proposed a new model that combines advanced CNN and Bidirectional Encoder Representations from Transformers (BERT) context embedding to predict hate speech in social media. This research trained contextual embedding on the datasets and used the learned information to identify objectionable language and hate speech in text. The paper evaluated supervised machine learning classifiers for bigoted and offensive content on Twitter using two datasets and found that advanced CNN context embeddings produced superior results. This research generated optimistic outcomes, which achieves 73% F1-score for Davidson dataset and 56% F1-score for TRAC-1 dataset [ABSTRACT FROM AUTHOR]

In this paper we analyze the problem of data-driven university management. We propose a concept of an intelligent strategic decision support system (ISDSS) to ensure that informed decisions are made in the management of higher education institutions. We show that the underlying task of various decision support tasks in university management is the analysis and forecasting of scientific and technological trends. We propose an approach to solving this task which includes determining promising emerging and existing technological areas, forecasting the further development of each of the selected areas, generating possible scenarios of their development and preparing suggestions for decision makers. [ABSTRACT FROM AUTHOR]

Big Data wave has led to a rapid increase in the amount of data being collected by organizations. While the accuracy and reliability of prediction models are often prioritized, the quality of the collected data is frequently overlooked. Poor data quality can result in the common problem of 'garbage in, garbage out'. Traditional measures of data quality, such as accuracy, consistency, completeness, and timeliness, are no longer adequate in the era of Big Data. Therefore, this paper proposes a taxonomy of data quality dimensions specifically for Big Data, addressing emerging challenges by formulating 20 dimensions and categorizing them into four distinct categories. [ABSTRACT FROM AUTHOR]

The paper discusses the possibility of increasing the effectiveness of strategic management of research and development activities in higher education institutions. To achieve this goal, the authors proposed an algorithm for selecting competencies, which allows for the distribution of university employees and students among research and development teams in accordance with their individual competencies for carrying out research and development work. The paper covers the problem statement of competence selection, an overview of natural language processing methods for algorithm development, problematic areas of the algorithm, and prospects for its implementation in university management. [ABSTRACT FROM AUTHOR]

Determination of macroeconomic policies in real-time requires assessing the correct information regarding current economic conditions. This statement spurred researchers to develop methods involving high-frequency data for risk analysis. This paper extends the quarterly growth-at-risk (GaR) approach by involving a machine-learning approach based on the Mixed-Frequency Data Sampling Quantile Regression Neural Network (MIDAS-QRNN) model. This paper shows that the MIDAS-QRNN model has the best prediction accuracy and can show good PDB nowcasting. The monthly financial GaR can detect unusual economic growth movements during the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]