15 citations · 36 across the 13 of their papers we have counts for
13 papers
PromptTSS: A Prompting-Based Approach for Interactive Multi-Granularity Time Series Segmentation
Ching Chang, Ming-Chih Lo, Wen-Chih Peng +1
Multivariate time series data, collected across various fields such as manufacturing and wearable technology, exhibit states at multiple levels of granularity, from coarse-grained…
Text2Freq: Learning Series Patterns from Text via Frequency Domain
Ming-Chih Lo, Ching Chang, Wen-Chih Peng
Traditional time series forecasting models mainly rely on historical numeric values to predict future outcomes.While these models have shown promising results, they often overlook…
COKE: Causal Discovery with Chronological Order and Expert Knowledge in High Proportion of Missing Manufacturing Data
Ting-Yun Ou, Ching Chang, Wen-Chih Peng
Understanding causal relationships between machines is crucial for fault diagnosis and optimization in manufacturing processes. Real-world datasets frequently exhibit up to 90% mis…
Team Triple-Check at Factify 2: Parameter-Efficient Large Foundation Models with Feature Representations for Multi-Modal Fact Verification
Wei-Wei Du, Hong-Wei Wu, Wei-Yao Wang +1
Multi-modal fact verification has become an important but challenging issue on social media due to the mismatch between the text and images in the misinformation of news content, w…
Look Around! A Neighbor Relation Graph Learning Framework for Real Estate Appraisal
Chih-Chia Li, Wei-Yao Wang, Wei-Wei Du +1
Real estate appraisal is a crucial issue for urban applications, which aims to value the properties on the market. Traditional methods perform appraisal based on the domain knowled…
Where Will Players Move Next? Dynamic Graphs and Hierarchical Fusion for Movement Forecasting in Badminton
Kai-Shiang Chang, Wei-Yao Wang, Wen-Chih Peng
Sports analytics has captured increasing attention since analysis of the various data enables insights for training strategies, player evaluation, etc. In this paper, we focus on p…
Track2Vec: fairness music recommendation with a GPU-free customizable-driven framework
Wei-Wei Du, Wei-Yao Wang, Wen-Chih Peng
Recommendation systems have illustrated the significant progress made in characterizing users' preferences based on their past behaviors. Despite the effectiveness of recommending…
Team Yao at Factify 2022: Utilizing Pre-trained Models and Co-attention Networks for Multi-Modal Fact Verification
Wei-Yao Wang, Wen-Chih Peng
In recent years, social media has enabled users to get exposed to a myriad of misinformation and disinformation; thus, misinformation has attracted a great deal of attention in res…
ShuttleNet: Position-aware Fusion of Rally Progress and Player Styles for Stroke Forecasting in Badminton
Wei-Yao Wang, Hong-Han Shuai, Kai-Shiang Chang +1
The increasing demand for analyzing the insights in sports has stimulated a line of productive studies from a variety of perspectives, e.g., health state monitoring, outcome predic…
Exploring the Long Short-Term Dependencies to Infer Shot Influence in Badminton Matches
Wei-Yao Wang, Teng-Fong Chan, Hui-Kuo Yang +3
Identifying significant shots in a rally is important for evaluating players' performance in badminton matches. While there are several studies that have quantified player performa…
Sequence-Aware Factorization Machines for Temporal Predictive Analytics
Tong Chen, Hongzhi Yin, Quoc Viet Hung Nguyen +3
In various web applications like targeted advertising and recommender systems, the available categorical features (e.g., product type) are often of great importance but sparse. As…
TrackNet: A Deep Learning Network for Tracking High-speed and Tiny Objects in Sports Applications
Yu-Chuan Huang, I-No Liao, Ching-Hsuan Chen +2
Ball trajectory data are one of the most fundamental and useful information in the evaluation of players' performance and analysis of game strategies. Although vision-based object…
On the Feature Discovery for App Usage Prediction in Smartphones
Zhung-Xun Liao, Shou-Chung Li, Wen-Chih Peng +1
With the increasing number of mobile Apps developed, they are now closely integrated into daily life. In this paper, we develop a framework to predict mobile Apps that are most lik…