11 citations · 18 across the 7 of their papers we have counts for
7 papers
ACL-Fig: A Dataset for Scientific Figure Classification
Zeba Karishma, Shaurya Rohatgi, Kavya Shrinivas Puranik +2
Most existing large-scale academic search engines are built to retrieve text-based information. However, there are no large-scale retrieval services for scientific figures and tabl…
Automatic Metadata Extraction Incorporating Visual Features from Scanned Electronic Theses and Dissertations
Muntabir Hasan Choudhury, Himarsha R. Jayanetti, Jian Wu +2
Electronic Theses and Dissertations (ETDs) contain domain knowledge that can be used for many digital library tasks, such as analyzing citation networks and predicting research tre…
ScanBank: A Benchmark Dataset for Figure Extraction from Scanned Electronic Theses and Dissertations
Sampanna Yashwant Kahu, William A. Ingram, Edward A. Fox +1
We focus on electronic theses and dissertations (ETDs), aiming to improve access and expand their utility, since more than 6 million are publicly available, and they constitute an…
Extractive Research Slide Generation Using Windowed Labeling Ranking
Athar Sefid, Jian Wu, Prasenjit Mitra +1
Presentation slides describing the content of scientific and technical papers are an efficient and effective way to present that work. However, manually generating presentation sli…
Document Domain Randomization for Deep Learning Document Layout Extraction
Meng Ling, Jian Chen, Torsten Möller +7
We present document domain randomization (DDR), the first successful transfer of convolutional neural networks (CNNs) trained only on graphically rendered pseudo-paper pages to rea…
Predicting the Reproducibility of Social and Behavioral Science Papers Using Supervised Learning Models
Jian Wu, Rajal Nivargi, Sree Sai Teja Lanka +8
In recent years, significant effort has been invested verifying the reproducibility and robustness of research claims in social and behavioral sciences (SBS), much of which has inv…
Cleaning Noisy and Heterogeneous Metadata for Record Linking Across Scholarly Big Datasets
Athar Sefid, Jian Wu, Allen C. Ge +5
Automatically extracted metadata from scholarly documents in PDF formats is usually noisy and heterogeneous, often containing incomplete fields and erroneous values. One common way…