Cultural heritage images are among the primary media for communicating and preserving the cultural values of a society. The images represent concrete and abstract content and symbolise the social, economic, political, and cultural values of the society. However, an enormous amount of such values embedded in the images is left unexploited partly due to the absence of methodological and technical solutions to capture, represent, and exploit the latent information. With the emergence of new technologies and availability of cultural heritage images in digital formats, the methodology followed to semantically enrich and utilise such resources become a vital factor in supporting users need. This paper presents a methodology proposed to unearth the cultural information communicated via cultural digital images by applying Artificial Intelligence (AI) technologies (such as Computer Vision (CV) and semantic web technologies). To this end, the paper presents a methodology that enables efficient analysis and enrichment of a large collection of cultural images covering all the major phases and tasks. The proposed method is applied and tested using a case study on cultural image collections from the Europeana platform. The paper further presents the analysis of the case study, the challenges, the lessons learned, and promising future research areas on the topic.
The flipped classroom approach is supporting a continuous interaction of students and instructors via online communication and strengthens the ability to solve problems by self-organization. This is especially important in medical domains, like the one of ENT-Head/Neck (ENT: Ear-Nose-Throat) surgeries and treatment. Surgical corrections of the nasal airways, like FESS (Functional Endoscopy Sinus Surgery) or in the field of plastic surgery, are the second frequent surgical interventions in otorhinolaryngology. They have to be prepared very carefully and airflow simulation with Computational Fluid Dynamics (CFD) is gaining importance for diagnostics here. New in silico procedures, like imaging, 3D modelling, CFD simulation and analysis, are to be part of a standard clinical pathway in the ENT domain in the near future. The project Rhinodiagnost.eu for instance will extend the morphological diagnostics by detailed functional analysis, i.e. the visualization of the nasal air stream and the physical analysis of its energetic. In order to train ENT specialists on the new diagnostic aids Rhinodiagnost.eu will provide flipped classroom online lessons using the tool “Mediathread” of the Columbia University, as learning environment.
In recent years, cross-disciplinary collaboration for increased knowledge extraction from diverse data sources has been at the heart of interdisciplinary research and related fields, such as Digital Humanities. In particular, knowledge extraction and preservation from cultural heritage data has received increased attention. In this paper, we introduce the ChIA project, a cross-disciplinary Digital Humanities project that aims to perform knowledge design, knowledge extraction and organisation by applying semantic as well as Artificial Intelligence (AI) tools on a set of Europeana cultural food images. The collaborative endeavour aims to increase cultural knowledge access and analysis possibilities of images for different user groups and stakeholders, such as content providers or for educational purposes.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.