• Digital Humanities and Cultural Heritage
• Publications close
• Research data close
• Other research products close
• ISPRS - International Archives of t... close
• ISTI Open Portal close
• OpenAIRE close

In this work, we investigate the performance of a proximity detection system for visitors in an indoor museum exploiting data collected from the crowd. More specifically, we propose a CrowdSensing-based technique for proximity detection. Users' smartphones can collect and upload RSS (Received Signal Strength) values of nearby Bluetooth tags to a backend server, together with some context-information. In turn, the collected data are elaborated with the goal of calibrating two proximity detection algorithms: a range-based and a learning-based algorithm. We embed the algorithms with R-app, a visiting museum application tested in the Monumental Cemetery's museum located in Piazza dei Miracoli, Pisa (IT). We detail in this work an experimental campaign to measure the performance improvements of the CrowdSensing approach with respect to state-of-the-art algorithms widely adopted in the field of proximity detection. Experimental results show a clear improvement of the performance when data from the crowd are exploited with the proposed architecture.

The democratization of artificial intelligence (AI) has brought substantial achievements in science, engineering disciplines, and society as a whole. New technologies based on large language models, multi-modal learning, embodied AI, and the quest for artificial general intelligence (AGI) promise to further change the world's landscape as we know it. At the same time, AI's rapid and uncontrolled evolution also poses serious risks to society, such as the concentration of power, exclusion, discrimination, and manipulation of reality. The keynote will present some experiences in AI democratization, including the us- age of explainable machine learning approaches for agronomists, NLP-based solutions for railway engineers, image processing techniques for the maintenance of riverbeds, and mobile data processing in road safety assessment. . The talk will outline the latest technological advancements in AI, e.g., in healthcare and science, and will show how large language models like ChatGPT and Bing Chat can solve long-standing requirements engineering (RE) problems. For example, requirements completeness can be easily checked and addressed with simple prompts, and model generation from requirements becomes a one-click task. The keynote will then describe the risks that current AI development poses to society. Besides the increasingly convincing deep fakes, and the widely discussed risks for privacy and reputation, we must be aware of the uncontrolled speed of AI evolution. As AI continues to advance, it will replace many jobs that require intellectual skills. This could lead to a significant number of people losing their jobs, as they may not have the necessary skills to adapt to the new labour market. People and entire countries that cannot exploit technological developments will be excluded from the game, and this will cause resentment and the possible emergence of new fundamentalism. The race for semiconductors is already creating hot spots and rifts between the superpowers.

Allocation of requirements to different teams is a typical preliminary task in large-scale system development projects. This critical activity is often performed manually and can benefit from automated requirements classification techniques. To date, limited evidence is available about the effectiveness of existing machine learning (ML) approaches for requirements classification in industrial cases. This paper aims to fill this gap by evaluating state-of-the-art language models and ML algorithms for classification in the railway industry. Since the interpretation of the results of ML systems is particularly relevant in the studied context, we also provide an information augmentation approach to complement the output of the ML-based classification. Our results show that the BERT uncased language model with the softmax classifier can allocate the requirements to different teams with a 76% F1 score when considering requirements allocation to the most frequent teams. Information augmentation provides potentially useful indications in 76% of the cases. The results confirm that currently available techniques can be applied to real-world cases, thus enabling the first step for technology transfer of automated requirements classification. The study can be useful to practitioners operating in requirements-centered contexts such as railways, where accurate requirements classification becomes crucial for better allocation of requirements to various teams.

The use of neural encodings has the potential to replace the commonly used polynomial fitting in the analysis of artwork surface based on Reflectance Transformation Imaging (RTI), as it has proved to result in more compact encoding with better relight quality, but it is still not widely used due to the lack of efficient implementations available to practitioners. In this work, we describe an optimized system to encode/decode neural relightable images providing interactive visualization in a web interface allowing multi-layer visualization and annotation. To develop it, we performed several experiments testing different decoder architectures and input processing pipelines, evaluating the quality of the results on specific benchmarks to find the optimal tradeoff between relighting quality and efficiency. A specific decoder has been then implemented for the web and integrated into an advanced visualisation tool. The system has been tested for the analysis of a group of ancient Roman bronze coins that present scarce readability and varying levels of preservation and that have been acquired with a multispectral light dome. Their level of corrosion and degradation, which in some cases hinders the recognition of the images, numerals, or text represented on them, makes the system testing particularly challenging and complex. Testing on such a real case scenario, however, enables us to determine the actual improvement that this new RTI visualization tool can offer to numismatists in their ability to identify the coins.

Abstract. The ERASMUS+ initiative funded, in 2019, a Capacity Building project named ERAMCA (Environmental Risk Assessment and Mitigation in Central Asia) to build up a study plan at the Master of Science level (120 ECTS in 2 academic years) open to Architects, Civil, Building, and Environmental Engineers to build-up a new generation of professionals able to work in a team with different specialities to document Cultural Heritage assets by considering environmental risks. These new professionals will be able to set up the helpful basic knowledge to plan future actions of preservation and enhancement by following the most recent development in the restoration field at the international level.The ERAMCA project started with a survey of the basic knowledge of architects and engineers in Uzbekistan and Tajikistan on the documentation of natural and anthropic risks.By considering the common knowledge, the Partners of the ERAMCA project (3 European Universities from Italy, Germany, and Croatia and 4 Universities from Uzbekistan and Tajikistan) realised a study plan by concentrating the teaching activity on the following disciplines: Geomatics, Restoration, Structure and Seismic Engineering, Hydrogeology and Geotechnics.ERAMCA project is in its last year of activity, and the final results can be presented and discussed among the Cultural Heritage Documentation community to offer possible suggestions for future actions to diffuse documentation strategies.

Abstract. The last decade has seen the development of a growing tendency to use the most modern technologies, in the field of Cultural Heritage, with the aim of digitizing and facilitating protection and conservation activities. Much research has focused on the development of innovative methods such as photogrammetry or Terrestrial Laser Scanners, in terms of reliability, precision, time and costs. In this research, however, the use of the smartphone was investigated by comparing the point clouds obtained via videogrammetry from smartphones, with those generated by different digital survey techniques, such as Terrestrial Laser Scanners and photogrammetry via SLR camera. Specifically, a smartphone was used and the comparison between point clouds was conducted based on four criteria: point clouds fitting, density evaluation, profiling and texture quality with the aim, therefore, of verifying the geometric reliability of the data and the quality of polygonal lines and the mesh/texture derived. The case study selected was the sepulchral monument of the Pascopepe Lambertini family (14th century), located in the Crypt of Santa Maria della Scala, the Cathedral of Trani (South of Italy). Finally, the research demonstrates how this methodology, in the documentation of heritage, allows for even greater portability and accessibility compared to other methodologies, maintaining widely acceptable standards of accuracy and therefore going to constitute a valid alternative in the documentation of historical heritage.

Abstract. Documentation of CH is paramount to preserve its memory and allow its study, especially when CH is meant to be dismantled, as for the architectures of World’s Fairs. Very few efforts are undertaken to achieve this goal and national and international institutions rarely adopt innovative digital technologies. Digital technologies and products like 3D digital models are successfully and largely applied in CH domains, while webGIS is less explored for CH. Nevertheless, 3D digital models are rarely adopted to accompany digital archives and Digital Humanities studies. Very few cases use 3D models for World’s Fairs’ study and documentation. WebGIS is not used for World’s Fairs, and are scarcely adopted and not fully exploited by Digital Humanities and digital archives for sharing data and information about CH. Turin 1911 is the first digital project aimed to virtually document, recreate, and study an entire World’s Fair. Combining digital technologies (geo-DB, 3D digital reconstruction, and 3D webGIS) with cataloging standards, Turin 1911 is a pioneering initiative that applies these innovations to Digital Humanities in order to share information online. In this paper, dedicated webGIS applications are developed for the Turin 1911 needs, reporting the designed procedure, challenges in the development phase, and potentialities for Digital Humanities research. Finally, BIM models are also integrated in webGIS apps, making visible no more visible architectures. The paper discusses how webGIS apps could become a way for sharing information and data, but also a working environment for Digital Humanities studies where the research takes place in 3D environments.