Web Science and Digital Libraries Research Group

  The 2023 International Conference on Document Analysis and Recognition (ICDAR 2023)  was held in person from August 21 to 26, 2023, with the requirement for all authors to personally present their papers at the conference. Exceptions were made only in cases where authors faced unavoidable circumstances that prevented their attendance. In such instances, synchronous video presentations were organized to facilitate their oral sessions. For authors unable to attend, their posters were featured through prerecorded teaser videos in addition to physical poster displays. The main conference, which took place from August 21 to 23, 2023, featured three keynote talks by distinguished speakers: Marti Hearst from UC Berkeley , Vlad Morariu from Adobe Research , and Seichi Uchida from Kyushu University . During the conference, I attended all the sessions, representing the LAMP-SYS lab of the  Web Science and Digital Library Research group (WS-DL). Day 1 (Main Conference) Day 1 of...

Introduction The realm of concerns surrounding reproducibility, replicability, and generalizability (RR&G) of findings has gained substantial attention within the social and behavioral sciences as well as artificial intelligence (AI). While these concerns have evolved over the past decade and have seen recognition in top-tier journals, they have recently extended their reach into the field of AI. Inconsistencies in terminologies have led to the adoption of precise definitions from Goodman et al. (2016) [7]. Reproducibility refers to consistent computational results under the same conditions, replicability involves achieving consistent results on similar datasets, and generalizability pertains to consistent results across different experimental contexts. AI's reproducibility studies have mostly targeted empirical and computational AI, focusing on open datasets, code availability, and metadata documentation. However, efforts towards the replicability of AI research have remained...