Copyright Literacy in the UK: a survey of librarians and other cultural heritage sector professionals

Based on a survey of UK library and information professionals and those who work in the cultural heritage sector, carried out in December 2014, this research sought to examine the levels of copyright „literacy?. The survey aimed to obtain responses from all sectors, however most responses were received from academic libraries. The research examined their knowledge of national and international copyright issues as well as copyright policies at an institutional level. The survey also explored the need for copyright education for new and existing professionals and suggested topics for inclusion in training activities. The findings suggest that levels of knowledge amongst UK professionals are higher than those in other countries who participated in the first phase of the project. UK institutions are also more likely to have a copyright policy and an individual with responsibility for copyright. The results should be of interest to library managers, library educators and those with responsibility for staff training

Diffusion Based Augmentation for Captioning and Retrieval in Cultural Heritage

Cultural heritage applications and advanced machine learning models are creating a fruitful synergy to provide effective and accessible ways of interacting with artworks. Smart audio-guides, personalized art-related content and gamification approaches are just a few examples of how technology can be exploited to provide additional value to artists or exhibitions. Nonetheless, from a machine learning point of view, the amount of available artistic data is often not enough to train effective models. Off-the-shelf computer vision modules can still be exploited to some extent, yet a severe domain shift is present between art images and standard natural image datasets used to train such models. As a result, this can lead to degraded performance. This paper introduces a novel approach to address the challenges of limited annotated data and domain shifts in the cultural heritage domain. By leveraging generative vision-language models, we augment art datasets by generating diverse variations of artworks conditioned on their captions. This augmentation strategy enhances dataset diversity, bridging the gap between natural images and artworks, and improving the alignment of visual cues with knowledge from general-purpose datasets. The generated variations assist in training vision and language models with a deeper understanding of artistic characteristics and that are able to generate better captions with appropriate jargon.Comment: Accepted at ICCV 2023 4th Workshop on e-Heritag

Research Perspectives on the Public Domain: Digital Conference Proceedings

The public domain is a subject of vital interest to legal scholars, but its implications are far reaching – indeed, the public domain concept is germane to subjects as diverse as film and media studies, economics, political science and organisational theory. It was a central purpose of the workshop to arrive at a workable definition of the public domain suitable for empirical investigation. The traditional definition (1) takes the copyright term as the starting point, and defines the public domain as “out of copyright”, i.e. all uses of a copyright work are possible. A second, more fine-grained definition (2) still relies on the statutory provisions of copyright law, and asks what activities are possible with respect to a copyright work without asking for permission (e.g. because use is related to “underlying ideas” not appropriating substantial expressions, or because use is covered by specific copyright exceptions). A third definition (3) includes as part of the public domain all uses that are possible under permissive private ordering schemes (such as creative commons licences). A forth definition (4) moves into a space that includes use that would formally be copyright infringement but is endorsed, or at least tolerated by certain communities of practice (e.g. machinima or fan fiction)

Digital image processing of the Ghent altarpiece : supporting the painting's study and conservation treatment

In this article, we show progress in certain image processing techniques that can support the physical restoration of the painting, its art-historical analysis, or both. We show how analysis of the crack patterns could indicate possible areas of overpaint, which may be of great value for the physical restoration campaign, after further validation. Next, we explore how digital image inpainting can serve as a simulation for the restoration of paint losses. Finally, we explore how the statistical analysis of the relatively simple and frequently recurring objects (such as pearls in this masterpiece) may characterize the consistency of the painter’s style and thereby aid both art-historical interpretation and physical restoration campaign

A study of existing Ontologies in the IoT-domain

Several domains have adopted the increasing use of IoT-based devices to collect sensor data for generating abstractions and perceptions of the real world. This sensor data is multi-modal and heterogeneous in nature. This heterogeneity induces interoperability issues while developing cross-domain applications, thereby restricting the possibility of reusing sensor data to develop new applications. As a solution to this, semantic approaches have been proposed in the literature to tackle problems related to interoperability of sensor data. Several ontologies have been proposed to handle different aspects of IoT-based sensor data collection, ranging from discovering the IoT sensors for data collection to applying reasoning on the collected sensor data for drawing inferences. In this paper, we survey these existing semantic ontologies to provide an overview of the recent developments in this field. We highlight the fundamental ontological concepts (e.g., sensor-capabilities and context-awareness) required for an IoT-based application, and survey the existing ontologies which include these concepts. Based on our study, we also identify the shortcomings of currently available ontologies, which serves as a stepping stone to state the need for a common unified ontology for the IoT domain.Comment: Submitted to Elsevier JWS SI on Web semantics for the Internet/Web of Thing

Strategies for the deployment of microclimate sensors in spaces housing collections

[EN] The study of the microclimate is pivotal for the protection and conservation of cultural heritage. This paper describes specifc procedures aimed at the deployment of microclimate sensors in spaces housing collections (e.g., museums) under diferent scenarios. The decision making involves a multidisciplinary discussion among museum manager, con¿ servator and conservation scientist and implies fve steps. Since the sensor¿s deployment depends on the number of available sensors, we have identifed two possible circumstances: (a) artwork-related deployment (i.e., there are as many sensors as the number of artworks) and (b) artwork-envelope-related deployment (i.e., the number of available sensors is less than the number of artworks). The former circumstance is advisable when the artwork is often moved from a museum to another one. The latter circumstance is usually the case of permanent collections, and, according to the Museum Scenario (MS), the related procedures can be further subdivided into basic (MSI and MSII) and advanced (MSIII and MSIV). Advanced procedures are preferable over basic procedures when several time series of microcli¿ mate data have been collected for at least one calendar year in several sampling points. All these procedures make it possible to design where to deploy sensors both in the case of an initial deployment and of optimisation of already installed sensors.This research was funded by the European Union's Horizon 2020 research and innovation program under grant agreement No.814624.Frasca, F.; Verticchio, E.; Peiró-Vitoria, A.; Grinde, A.; Bile, A.; Chimenti, C.; Conati Barbaro, C.... (2022). Strategies for the deployment of microclimate sensors in spaces housing collections. Heritage Science. 10(1):1-17. https://doi.org/10.1186/s40494-022-00831-111710