ACM Journal on Computing and Cultural Heritage最新文献

Decision-making (DM) is the backbone of the Conservation and Restoration (CnR) of Cultural Heritage (CH). The demands of the DM process for information organization and management have raised issues that the CnR community attempts to solve by creating DM-support tools and systems, which, among others, exploit Semantic Web (SW) technologies. Regarding the tools and systems that focus on the DM process of selecting an intervention option (CnR-DM-I), they present benefits, as well as limitations, regarding the i) completeness of representation of the relevant knowledge in a unified manner, ii) facilitation of recording the CnR-DM-I process per se, in terms of the problem at hand as well as the intervention parameters, requirements and criteria, and iii) recommendation and further exploration of CnR intervention options in a systematic manner. This work proposes an ontology-based framework as a means to overcome those limitations. The proposed framework (DS-CnRI) sets at its core a formal ontology which provides the necessary entities to represent expert knowledge related to CnR-DM-I. The ontology also includes rules which provide useful inferences to assist the CnR-DM-I process. The proposed framework has been deployed and evaluated in collaboration with conservators. Initial evaluation results show that the framework assists conservators in CnR-DM-I to detect and select the most suitable intervention options, to better understand the limitations of different options and to document the process of reaching their decision.

Interactive multimedia art shows a complex nature as it is time- and process-based, interconnected with technology, derived by the participation of several authors (artists, technicians, performers, to name a few) and an audience, and it is strongly tied to the moment and space of the original exhibition. These characteristics make the preservation of these artworks a multifaceted process. Building on the foundations developed since the 2000s by international projects focused on preserving and restoring these new art forms, the paper proposes an original model for achieving dynamic preservation, called “the multilevel preservation model”. Since it is no longer possible to guarantee physical integrity for interactive multimedia artworks, dynamic preservation involves recording all the changes that occur to display the artworks in the future. In other words, it makes it possible to record the dynamic authenticity of artworks. The model proposes two fundamental properties: multiple layering, which allows to handle different levels of information about the artwork; multiple belongingness, which allows to represent the dynamic authenticity of the artwork at the archival level and achieving dynamic preservation.

The paper demonstrates the high-level implementation of the model by presenting a case study: the reactivation and preservation of an analog video art performance from the 1970s. This case study proposes an interesting real scenario for testing the model, as the reactivation process involved the migration of the entire analog technological apparatus of the artwork into the digital domain.

Social media platforms play an increasingly important role in cultural communication as society develops, attracting promotions and discussions about digital cultural and creative products (CACPs). This research investigates the cultural collaboration between Tencent and Dunhuang Research Academy (Dunhuang Academy) and analyzes user evaluation of integrating cultural heritage education into CACPs. We obtained data through Weibo and compared user evaluations and semantic social network analysis of digital CACPs, including interactive products, games, and music. Results indicated that users were more interested in landscapes, dubbing, and user-generated content (UGC) for interactive products, character versions, posters and skills for games, and singers and songs for concerts. Semantic social network analysis was also used to explore the Dunhuang CACP Circle. Scant studies evaluate the usefulness of integrating cultural heritage into different digital CACPs, especially in Asia. Our suggestions help promoters understand user needs for digital CACPs and better user experience and value.

Visualizing data allows us to interactively explore and analyse datasets, which can be dynamic, noisy, and heterogeneous. In the field of cultural heritage, more and more museums and cultural institutions are exploiting modern visualisation systems to disseminate their content in an attractive, usable, and interactive way, which involves displaying data on spatiotemporal maps. The SeMap project has built an online resource that displays more than 200,000 cultural objects from the CER.ES repository, which was created by a network of more than 100 Spanish museums. In this repository, SeMap analyses the textual data to extract meaning, showing the results on interactive spatiotemporal maps, which bring new experiences and perspectives around such objects. This article explains the strategies to visualize such information, as well as an evaluation of the finalized tool by second-year university students on a Data Science graduate programme, as part of the subject of Data Visualization. Results show that most of the students find the tool usable although they do not fully understand the cultural-related content.

Weathering effects caused by physical, chemical, or biological processes result in visible damages that alter the appearance of stones’ surfaces. Consequently, weathered stone monuments can offer a distorted perception of the artworks to the point of making their interpretation misleading. Being able to detect and monitor decay is crucial for restorers and curators to perform important tasks such as identifying missing parts, assessing the preservation state, or evaluating curating strategies. Decay mapping, the process of identifying weathered zones of artworks, is essential for preservation and research projects. This is usually carried out by marking the affected parts of the monument on a 2D drawing or picture of it. One of the main problems of this methodology is that it is manual work based only on experts’ observations. This makes the process slow and often results in disparities between the mappings of the same monument made by different experts. In this paper, we focus on the weathering effect known as “scaling”, following the ICOMOS ISCS definition. We present a novel technique for detecting, segmenting, and classifying these effects on stone monuments. Our method is user-friendly, requiring minimal user input. By analyzing 3D reconstructed data considering geometry and appearance, the method identifies scaling features and segments weathered regions, classifying them by scaling subtype. It shows improvements over previous approaches and is well-received by experts, representing a significant step towards objective stone decay mapping.

The use of Internet of Things (IoT) technology to improve access to cultural heritage sites and enhance visitor engagement has become increasingly popular. Nevertheless, there is a lack of studies evaluating the actual benefits of such technology on visitors’ experiences, which is crucial to improve the IoT adoption in this field. To address this issue, we present an exploratory study we performed to understand whether and how an IoT-based game affects the experience of museum visitors. The game is played after a traditional guided tour; it was created following a meta-design approach to enable domain experts, such as museum professional guides, to adapt the game to specific visitors and/or situations. The study involved a total of 18 participants who took a guided tour of the Museum of Natural Sciences of the University of Bari and played the game afterward. Both quantitative and qualitative data were collected. The study results show that playing the IoT-based game positively affects the experience of visitors and, in particular, their emotions. Visitor workload was also found to be greater than that of a guided tour, but this had no negative impact on visitor experience. The study findings have implications for the design and development of future IoT games to enhance engagement and to improve the experience of visitors at cultural heritage sites.

The Jiangnan region of China boasts a superior natural environment, and the combination of cities and mountains that form its historical landscape give it unique attributes. Local chronicles serve as an abundant source of information for visualizing this historic environment. This study aims to achieve two objectives: (1) to use digital humanities methods to process textual information in local chronicles, and (2) to visualize the historical relationships formed by the mountains and cities. In this study, we chose Suzhou, Zhenjiang, Jiaxing, Huzhou, and Songjiang in the Jiangnan region during the Qing Dynasty as our research sites. Through textual analysis and data visualization, we classified and reconstructed the historical spatial patterns of cities and mountains. As a result, our study provides a reproduction of the past spatial relationship between cities and mountains in the Jiangnan region during the Qing Dynasty. The research findings indicate that natural mountains play multiple roles in shaping the historical urban environment, particularly in terms of cultural images, notably the symbolic function they hold. The preservation of the urban natural environment, as seen through the vantage point of the historical landscape, is a critical component in contemporary urban development. The digital humanities methodologies utilized within this paper can be extended to other cultural heritage research, based on pre-existing local historical literature.

Recently, recommendation systems have become one of the important elements for sales and marketing, and their application is almost essential in the shopping and cultural industries. Despite the increase in online exhibitions and the growing audience engaging with artworks in digital spaces, the utilization of artwork recommendation systems remains inadequate. Thus, this study proposes an artwork recommendation system, which provides artwork groups based on a visual clustering technique and user preferences with WikiArt datasets. The visual attributes of artworks were extracted using VGG16, and K-means clustering was utilized to group a set of images according to their feature similarities. To generate recommendations, new artworks were randomly selected from particular clusters, taking into account users’ preferences. Then, an experiment was conducted to investigate whether the recommended artworks satisfied the users. The statistical results indicate that users’ perceived satisfaction with the recommended artworks is notably more positive compared to their satisfaction with traditional suggested artworks. Based on this study’s findings, we present implications and limitations for future research.

Research domain and Problem: HBIM modelling from point cloud data has become a crucial research topic in the last decade since it is potentially considered the central data model paving the way for the digital heritage practice beyond digitization. Reality Capture technologies such as terrestrial laser scanning, drone-mounted LiDAR sensors and photogrammetry enable the reality capture with a sub-millimetre accurate point cloud file that can be used as a reference file for Heritage Building Information Modelling (HBIM). However, HBIM modelling from the point cloud data of heritage buildings is mainly manual, error-prone, and time-consuming. Furthermore, image processing techniques are insufficient for classification and segmenting of point cloud data to speed up and enhance the current workflow for HBIM modelling.

Due to the challenges and bottlenecks in the scan-to-HBIM process, which is commonly criticized as complex with its bespoke requirements, semantic segmentation of point clouds is gaining popularity in the literature.

Research Aim and Methodology: Therefore, this paper aims to provide a thorough critical review of Machine Learning and Deep Learning methods for point cloud segmentation, classification, and BIM geometry automation for cultural heritage case study applications.

Research findings: This paper files the challenges of HBIM practice and the opportunities for semantic point cloud segmentation found across academic literature in the last decade. Beyond definitions and basic occurrence statistics, this paper discusses the success rates and implementation challenges of machine and deep learning classification methods.

Research value and contribution: This paper provides a holistic review of point cloud segmentation and its potential for further development and application in the Cultural Heritage sector. The critical analysis provides insight into the current state-of-the-art methods and advises on their suitability for HBIM projects. The review has identified highly original threads of research, which hold the potential to significantly influence practice and further applied research.

HBIM, Point Cloud, Semantic Segmentation, Classification, Machine Learning, Deep Learning

Knowledge annotation and mining from ancient Chinese classics have become a new trend in digital humanities research in China, and historical persons get the most attention. However, few studies have focused exclusively on person names, which is rather important for understanding Chinese traditional culture. This study focused on person names in Chinese Pre-Qin classics. We conducted a humanities computing study on Pre-Qin person name knowledge through an in-depth categorization and manual annotation of person name components. The results included the disambiguation of person names, a statistical distribution of famous persons, patterns in name components, and statistical analysis of name components. Furthermore, machine learning methods on the NER of person name knowledge were also examined, indicating the feasibility of future related research.