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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.

A roadmap is proposed that defines a systematic approach for craft preservation and its evaluation. The proposed roadmap aims to deepen craft understanding so that blueprints of appropriate tools that support craft documentation, education, and training can be designed while achieving preservation through the stimulation and diversification of practitioner income. In addition to this roadmap, an evaluation strategy is proposed to validate the efficacy of the developed results and provide a benchmark for the efficacy of craft preservation approaches. The proposed contribution aims at the catalyzation of craft education and training with digital aids, widening access and engagement to crafts, economizing learning, increasing exercisability, and relaxing remoteness constraints in craft learning. Heritage, 6 (7) ISSN:2571-9408

L’articolo racconta l’esperienza del D4GNA - Dataset per il Geoportale Nazionale dell’Archeologia nato nell’ambito del progetto ARIADNEplus (Advanced Research Infrastructure for Archaeological Dataset Networking in Europe - plus), conclusosi lo scorso 31 dicembre. Il contributo parte dal contesto più ampio del progetto europeo per addentrarsi nel mondo dei dati archeologici italiani; la soluzione tecnologica, la standardizzazione, la dematerializzazione e la condivisione in rete dei dati sono i temi toccati in questo percorso che ci illustra il procedere verso un obiettivo virtuoso: il Geoportale Nazionale per l’Archeologia (GNA). Il GNA, realizzato dall’Istituto Centrale per l’Archeologia (ICA) e che sarà in rete dal 10 luglio 2023, è il punto di accesso nazionale per accogliere e consultare gli interventi archeologici svolti sotto la direzione scientifica del Ministero della Cultura (MiC), le indagini archeologiche condotte da università e altri enti di ricerca, nonché altre banche dati territoriali.

Western thought in European history was mainly affected by the image of the world created during the Middle Ages and Renaissance. The most popular reason to travel during the Middle Ages was taking a pilgrimage. Jerusalem, Rome, and Santiago de Compostela were the most popular destinations. It is not surprising that a lot of works written by travellers as guides for pilgrims exist. By the beginning of the Renaissance, a more precise image of the world was defined, thanks to the discovery of ancient geographical models, especially the work of Ptolemy. The Italian National Research Project (PRIN) IMAGO --- Index Medii Aevi Geographiae Operum --- (2020-2023) aims to provide a systematic overview of the medieval and renaissance Latin geographical literature using the Semantic Web technologies and the LOD paradigm. Indeed, until now, this literature has not been studied using digital methods. In particular, this article presents how we formally represented the knowledge about the toponyms, or place names, in the IMAGO ontology. To maximise the interoperability, we developed the IMAGO ontology as an extension of two reference vocabularies: the CIDOC CRM and its extension FRBRoo, including its in-progress reformulation, LRMoo. Furthermore, we used Wikidata as reference knowledge base. As case study, we chose to represent the knowledge related to the toponyms cited by the Italian poet Dante Alighieri in his Latin works. We carried out a first experiment for visualising the knowledge about these toponyms on a map and in the form of tables and CSV files.

AbstractNative language identification (NLI) is the task of training (via supervised machine learning) a classifier that guesses the native language of the author of a text. This task has been extensively researched in the last decade, and the performance of NLI systems has steadily improved over the years. We focus on a different facet of the NLI task, i.e. that of analysing the internals of an NLI classifier trained by an explainable machine learning (EML) algorithm, in order to obtain explanations of its classification decisions, with the ultimate goal of gaining insight into which linguistic phenomena ‘give a speaker’s native language away’. We use this perspective in order to tackle both NLI and a (much less researched) companion task, i.e. guessing whether a text has been written by a native or a non-native speaker. Using three datasets of different provenance (two datasets of English learners’ essays and a dataset of social media posts), we investigate which kind of linguistic traits (lexical, morphological, syntactic, and statistical) are most effective for solving our two tasks, namely, are most indicative of a speaker’s L1; our experiments indicate that the most discriminative features are the lexical ones, followed by the morphological, syntactic, and statistical features, in this order. We also present two case studies, one on Italian and one on Spanish learners of English, in which we analyse individual linguistic traits that the classifiers have singled out as most important for spotting these L1s; we show that the traits identified as most discriminative well align with our intuition, i.e. represent typical patterns of language misuse, underuse, or overuse, by speakers of the given L1. Overall, our study shows that the use of EML can be a valuable tool for the scholar who investigates interlanguage facts and language transfer.