Purpose – The purpose of this paper is to understand music sharing behaviour on social networking services (SNS). This study suggests and examines a research model which focuses on the influences of user motivations, such as self‐expression, ingratiation, altruism, and interactivity, on music sharing behaviour in SNS through social motivation factors.Design/methodology/approach – Data were collected from 153 Korean SNS (i.e. Cyworld, Naver Blog, Daum Blog, and Tistory) users, who have experience in purchasing music and legally sharing it on SNS. The partial least squares method was used to analyse the measurement and structural models.Findings – The study shows that interactivity, perceived ease of use, self‐expression, social presence, and social identity are significant positive predictors of music sharing intention on SNS.Research limitations/implications – This research is significant in light of recent interest in user activities in SNS. Better understanding of the music sharing behaviour on SNS can ...

Brain-computer interfaces (BCIs) establish a communication pathway between the human brain and external devices by decoding neural signals. This study focuses on enhancing the classification of Motor Imagery (MI) within BCI systems by leveraging advanced machine learning and deep learning techniques. The accurate classification of electroencephalogram (EEG) data is crucial for enhancing BCI performance. The BCI architecture processes electroencephalography signals through three critical stages: data pre-processing, feature extraction, and classification. The research evaluates the performance of five traditional machine learning classifiers- K-Nearest Neighbors (KNN), Support Vector Classifier (SVC), Logistic Regression (LR), Random Forest (RF), and Naive Bayes (NB)-using the “PhysioNet EEG Motor Movement/Imagery Dataset”. This dataset encompasses EEG data from various motor tasks, including both actual and imagined movements. Among the traditional classifiers, Random Forest achieved the highest accuracy of 91%, underscoring its efficacy in motor imagery classification within BCI systems. In addition to conventional approaches, the study also explores deep learning techniques, with Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks yielding accuracies of 88.18% and 16.13%, respectively. However, the proposed hybrid model, which synergistically combines CNN and LSTM, significantly surpasses both traditional machine learning and individual deep learning methods, achieving an exceptional accuracy of 96.06%. This substantial improvement highlights the potential of hybrid deep learning models to advance the state of the art in BCI systems, offering a more robust and precise approach to motor imagery classification.

The exponential growth in the number of publications and the prevalence of interdisciplinary research in recent years call for new approaches for analyzing how topics in science are evolving at large. This paper proposes TermBall, a framework that tracks and predicts fine-grained topic evolution in terms of the evolution types: emergence, growth, shrinkage, survival, merging, splitting, and dissolution. TermBall builds the knowledge structure, which is a weighted dynamic network of co-occurring keywords in the literature, and then discovers key topic structures that consist of keywords and their relationships by performing community detection methods. Based on the topic structures, TermBall provides two applications: (1) Retrospective application to identify topic evolution in the past and (2) Predictive application to forecast upcoming topic evolution type based on the structural and temporal features of the topic structures. For the evaluation, we built the knowledge structure by applying TermBall to 19 million articles in PubMed that were published from 1980 to 2014. We conducted qualitative analysis on the derived topic evolution types and quantitative analysis on the prediction results. As a result, our qualitative analysis reveals that TermBall is able to find various topic evolution types from the knowledge structure and also can predict how topics will evolve after five years with an accuracy of 83%.

Dietary habits greatly influence one's health. Particularly, for the patients who suffer from metabolic syndrome, maintaining a healthy dietary habit is crucial to their health. In this paper, we aim to study the dietary behavior of such patients and design a system that can support their difficulties during food plating and eating process. We implemented a prototype to support the patient's eating speed, food portion control and calorie/sodium management. The preliminary evaluation results show both positive and negative aspects of our approach.