1,720,971 research outputs found
Cultural heritage digital twin: modeling and representing the visual narrative in Leonardo Da Vinci’s Mona Lisa
No full textIn this paper, Artificial Intelligence/Knowledge Representation methods are used for the digital modeling of cultural heritage elements. Accordingly, the new concept of digital cultural heritage twin is presented as composed of a physical component and an immaterial component of the cultural entity. The former concerns the physical aspects, i.e. style, name of the artist, execution time, dimension, etc. The latter represents the emotional and intangible aspects transmitted by the entity, i.e. emotions, thoughts, opinions. In order to digitally model the physical and immaterial components of the twin, the Narrative Knowledge Representation Language has been formally introduced and described. It is particularly suitable for representing the immaterial aspects of the cultural entity, as it is capable of modeling in a simple but rigorous and efficient way complex situations and events, behaviours, attitudes, etc. As an experiment, NKRL has been adopted for representing some of the most relevant intangible items of the visual narrative underlying the hidden painting that lies beneath the Mona Lisa (La Gioconda) image painted by Leonardo Da Vinci on the same poplar panel. Real-time application of the resulting knowledge base opens up novel possibilities for the development of virtual objects, chatbots and expert systems, as well as the definition of semantic search platforms related to cultural heritage
Ensemble-based community detection in multilayer networks
No full textThe problem of community detection in a multilayer network can effectively be addressed by aggregating the community structures separately generated for each network layer, in order to infer a consensus solution for the input network. To this purpose, clustering ensemble methods developed in the data clustering field are naturally of great support. Bringing these methods into a community detection framework would in principle represent a powerful and versatile approach to reach more stable and reliable community structures. Surprisingly, research on consensus community detection is still in its infancy. In this paper, we propose a novel modularity-driven ensemble-based approach to multilayer community detection. A key aspect is that it finds consensus community structures that not only capture prototypical community memberships of nodes, but also preserve the multilayer topology information and optimize the edge connectivity in the consensus via modularity analysis. Empirical evidence obtained on seven real-world multilayer networks sheds light on the effectiveness and efficiency of our proposed modularity-driven ensemble-based approach, which has shown to outperform state-of-the-art multilayer methods in terms of modularity, silhouette of community memberships, and redundancy assessment criteria, and also in terms of execution times
Approximate Matching in ACSM Dissimilarity Measure
No full textAbstractThe paper introduces a new patch-based dissimilarity measure for image comparison employing an approximation strategy. It extends the Average Common Sub-matrix measure computing the exact dissimilarity among images. In the exact method, dissimilarity between two images is obtained by considering the average area of the biggest square sub-matrices in common between the images, by exact match of the extracted sub-matrices pixel by pixel. As an extension, the proposed dissimilarity measure computes an approximate match between the sub-matrices, which is obtained by omitting a controlled number of pixels at a given column offset inside the sub-matrices. The proposed dissimilarity measure is extensively compared with other well-known approximate methods for image comparison in the state-of-the-art. Experiments demonstrate the superiority of the proposed approximate measure in terms of execution time with respect to the exact method, and in terms of retrieval precision with respect to the other state-of-the-art methods
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Raw Earth Buildings and Industry 4.0: An Overview of the Technology and Innovation of the MUD-MADE Project
No full textResearch on digital production technologies for the building sector, although several decades behind other sectors, is beginning to become more and more systematic. The use of natural materials such as raw earth makes the sustainability of such processes even more pronounced than current building solutions. Despite this, many limitations still prevent the use of digital technologies employing raw earth for construction from becoming current. The article investigates the state of research on the topic, identifying the reasons for current limitations. It also describes the MUD-MADE research project that aims to overcome these limitations and make the use of digitally fabricated raw earth components for the building sector a reality. This project proposes a novel artificial intelligence-supported workflow for designing raw earth building components produced with digital manufacturing technology. The workflow can support the designer in a multi-objective optimization involving different performances (e.g., thermal, structural, acoustic) by saving material and maintaining feasibility. The workflow exploits parametric design to set a predefined visual script able to support the user. Indeed, the predefined script will allow the user to design a building component by selecting (or creating) different possible external shapes and infill geometries. The designer can include information about the local material and the available technology to digitally manufacture the component directly in the predefined code. In addition, the predefined script sets the boundary conditions and priorities for the expected performances. Moreover, performance priorities are defined by the user based on the requirements of the component to be achieved. Finally, artificial intelligence, exploiting the artificial neural network (ANN) will support the designer by automatically identifying the optimal configuration among the possible combinations of parameters and generative algorithms
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Interpretability of Machine Learning Models for Breast Cancer Identification: A Review
No full textOver recent years, machine learning models have enhanced breast cancer detection, especially in its early stages. Nevertheless, their integration into clinical practices remains limited despite their proven efficacy in early-stage detection among women. However, the results obtained by these approaches are poorly interpretable. This study seeks to demystify early-stage breast cancer detection and boost clinicians' trust in these methods by leveraging eXplainable Artificial Intelligence (XAI). This research underscores the potential of XAI as a foundational step to initiate conversations about adopting supportive AI tools in the clinical sphere. By incorporating these XAI methods, clinicians can better understand why a specific prediction has been made, promoting trust and facilitating more informed decision-making in breast cancer detection and treatment. This study uniquely investigates the potential of advanced XAI techniques to enhance the trustworthiness and reliability of machine learning models, specifically in the early detection and diagnosis of breast cancer. The different XAI approaches are critically reviewed, underlying the current limitations and proposing future work directions
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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