The Leonardo Code: Deciphering 50 Years of Artistic/Scientific Collaboration in the Texts and Images of Leonardo Journal, 1968-2018

International audienceLeonardo (1968-present), published by MIT Press, is the leading international peer-reviewed publication on the relationship between art, science and technology, making it an ideal dataset to analyze the emergence of such complex collaborations over time. To identify and analyze both the visible and latent interaction patterns, the research employs different granularities of data (article texts, images, publication dates, authors, their places of affiliation and disciplines) as part of a multimodal approach. Using a convolutional neural network, we examined the features of the images to analyze the modes of representing (and actually doing) art, science or engineering. We paired these features with information extracted using text mining to examine the relationships between the visual and the textual over time

The Tell-Tale Hat: Surfacing the Uncertainty in Folklore Classification

This dataset contains the texts of 31,088 Danish legends and descriptions of everyday life collected by the folklorist Evald Tang Kristensen from 1867 to 1924. The texts were digitized via OCR of printed editions. We have applied basic orthographic normalization and OCR correction to the texts, but otherwise they appear as originally printed. The input.txt file contains one story per line, prefaced by two tab-delimited metadata fields: a unique identifier for the story with the following format: COLLECTION_VOLUME_CHAPTER_NUMBER and the high-level classification (of 36 possible categories) assigned to the story by the folklorist when preparing the stories for publication. The accompanying code can be run on this input or any other set of texts with a single classifier assigned to each story to generate the naïve Bayes confusion matrix described in the paper

[I want to dance]: Comparative K-Pop Choreography Analysis through Deep-Learning Pose Estimation,

International audienceCritics have long noted the strong visual aspects of K-pop, with the videos for newly released songs garnering millions of hits in a very short time span. A key feature of many Kpop videos is the dancing. Although many of the official videos are not solely dance focused, incorporating aspects of visual storytelling, nearly all of Kpop videos include some form of dance. In addition to the « main » video for a Kpop release, the release of a dance video, or a dance rehearsal video, focusing exclusively on the dances has become common practice. These videos allow fans to learn and practice the dance, thereby increasing the kinesthetic connection between fans and their idols. At the same time, it affords an opportunity to explore the « dance vocabulary » of Kpop dances. While there are well-known Kpop choreographers who work with the Kpop idols to create their dances, there is little documentation of these dances beyond the dance videos themselves. In our work, we develop a series of methods for (a) identifying dance sequences in Kpop videos, irrespective of whether they are dance videos (b) develop a series of classifiers for the navigation of a large scale Kpop video corpus and (c) apply deep learning methods to identify dancers and their body positions. Taken together, these approaches pave the way for the development of a macroscope for the study of Kpop videos, allowing researchers to identify patterns in the Kpop space, explore dynamic change in features such as color space, or interrogate the differences in visual representations of male and female performers at an aggregate scale. Importantly, as pose estimation has become more accurate, these methods allow us to begin the process of inferring the dance vocabulary of Kpop and start the process of tracing transcultural choreographic flows

The Tell-Tale Hat: Surfacing the Uncertainty in Folklore Classification

Classification is a vexing problem in folkloristics. Although broad genre classifications such as "ballad", "folktale", "legend", "proverb", and "riddle" are well established and widely accepted, these formal classifications are coarse and do little more than provide a first level sort on materials for collections that can easily include tens, if not hundreds, of thousands of records. Many large collections of folklore have been classified using systems designed for very specific tasks, usually related to early theories about the spread of folk narrative. Beyond the straightforward parsing of folk expressions into easily recognized formal genres (e.g. ballad, riddle, joke, legend, fairytale, etc.), the overarching emphasis of these schemas is on topic indexing. Perhaps best known of these indices is the ATU index of fairy tales, designed to assist scholars who are interested in the comparison of fairy tales from one or more cultures. Another well-known index, the motif index of folk literature compiled by Stith Thompson (1955-58), is designed to assist scholars in discovering the relationships between complete narratives and their component parts, as well as the movement of motifs across time and space, where the motif is conceptualized as "the smallest element in a tale having a power to persist in tradition." Other genre specific classification schemes include the Migratory Legend [ML] catalog and Danmarks gamle Folkeviser [DgF]. More collection specific indices include the Child Ballads and, of particular interest to this study, the typological indices to the Danish folklore collector Evald Tang Kristensen's legend collections

Semi-supervised morphosyntactic classification of Old Icelandic.

We present IceMorph, a semi-supervised morphosyntactic analyzer of Old Icelandic. In addition to machine-read corpora and dictionaries, it applies a small set of declension prototypes to map corpus words to dictionary entries. A web-based GUI allows expert users to modify and augment data through an online process. A machine learning module incorporates prototype data, edit-distance metrics, and expert feedback to continuously update part-of-speech and morphosyntactic classification. An advantage of the analyzer is its ability to achieve competitive classification accuracy with minimum training data

[POSTER] The Digital Analysis of Syriac Handwriting (DASH) Project: Augmenting Manuscript Studies via Interactive Scriptcharts and IIIF

The DASH project uses recent advances in digital handwriting analysis to advance paleographic studies of the Aramaic dialect of Syriac in the context of Syriac Christianity, providing a model for similar endeavors in other languages. The project is based upon a curated database of over 67,000 individually identified letter images from 154 digitized Syriac manuscripts that have been shared by their hosting institutions via IIIF. The web environment developed for the project includes an interactive, customizable scriptchart generator that provides a range of options for viewing and comparing letter instances in context, as well as an embedded IIIF viewer. This work has led to a more nuanced evaluation of how, why, and when Estrangela and Serto letter forms appear in Syriac manuscripts, and continues to enhance understanding of manuscript provenance, scribal attribution, and chronology