Computing and Information Science Research
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Item Data from: Castles, Battlefields, and Continents: A Dataset of Maps from Literature DatasetBax, Axel; Mimno, David; Wilkens, Matthew (2025)These files contain data supported results in Bax et al. Castles, Battlefields, and Continents: A Dataset of Maps from Literature. In Bax et al. we found: Maps are not common in novels. It is not obvious that they are necessary at all. Yet maps do appear in some novels. Why and to what ends? To answer these questions, scholars need a large collection of novels that contain maps. We develop a computational system to identify maps from page images and apply it to a large historical corpus of fiction. We deploy a three part workflow using an ensemble of three finetuned EfficientNet convolutional neural network (CNN) classifiers, Contrastive Language-Image Pre-training (CLIP), and human annotation to identify 2,622 maps in over 32 million pages of fiction published 1800–1928. We find that 1) maps are rare, making up 0.008% of all pages (1.7% of novels contain at least one map) 2) “map novels” were most common at the turn of the 20th century, 3) maps mostly appear on endpapers or front matter, 4) only 43% of map novels contain references to maps in their library MARC records, 5) 25% of maps depict fictional settings, 6) 70% of maps represent areas at a regional or larger scale, and 7) map novels contain more spatial language than non-map novels.Item Data from: Grab-n-Go: On-the-Go Microgesture Recognition with Objects in HandLee, Chi-Jung; Li, Jiaxin; Yu, Tianhong Catherine; Zhang, Ruidong; Gunda, Vipin; Guimbretiere, Francois; Zhang, Cheng (2025)These files contain data supporting all results reported in Lee et. al. Grab-n-Go: On-the-Go Microgesture Recognition with Objects in Hand. As computing devices become increasingly integrated into daily life, there is a growing need for intuitive, always-available interaction methods — even when users’ hands are occupied. In this paper, we introduce Grab-n-Go, the first wearable device that leverages active acoustic sensing to recognize subtle hand microgestures while holding various objects. Unlike prior systems that focus solely on free-hand gestures or basic hand-object activity recognition, Grab-n-Go simultaneously captures information about hand microgestures, grasping poses, and object geometries using a single wristband, enabling the recognition of fine-grained hand movements occurring within activities involving occupied hands. A deep learning framework processes these complex signals to differentiate among 30 distinct microgestures organized into 5 grasping poses. In a user study with 10 participants and 25 everyday objects, Grab-n-Go achieved an average recognition accuracy of 92.0%. Furthermore, a follow-up study expanded our dataset to include an additional 10 deformable objects, and we will release this enriched dataset to the research community. These results underscore the potential of Grab-n-Go to provide seamless, unobtrusive interactions without requiring modifications to existing objects.Item Data containing transaction history and visual traits of eight highly valued Non-fungible token (NFT) collectionsSerneels, Sven; Cho, Jason B.; Matteson, David S. (2022-08-12)These data contain transaction history between the project launch and March 31, 2022, as well as visual traits at the token level of eight highly valued Non-fungible token (NFT) collections: Aurory, Autoglphs, Bored Ape Kennel Club, Bored Ape Yacht Club, Cryptopunks, Degenerate Ape Academy, Meebits and Mutant Ape Yacht Club. Non-fungible tokens have recently emerged as a novel blockchain hosted financial asset class that has attracted major transaction volumes. Investment decisions rely on data and adequate preprocessing and application of analytics to them. Both owing to the non-fungible nature of the tokens and to a blockchain being the primary data source, NFT transaction data pose several challenges not commonly encountered in traditional financial data. The article named "Non-fungible token transactions: data and challenges" written by Jason B. Cho, Sven Serneels, and David S. Matteson uses these data to broadly explore such challenges which include price differentiation by token traits, the possible existence of lateral swaps, and wash trades in the transaction history, and finally, severe volatility.