1,720,960 research outputs found
A deep learning approach to classify country and value of modern coins
No full textThe use of Artificial Intelligence (AI) to preserve and promote cultural heritage has experienced significant growth in recent years. Among the various areas of cultural heritage, numismatics have emerged as a particularly promising field where we can develop AI solutions. Numismatics refers to the study of coins, tokens, paper money, and medals, which play a critical role in understanding human history and culture. However, there are still limited resources available to help researchers and collectors in the identification of coins. This is due to the vast number of coins in circulation, which presents a significant challenge in developing smart tools for classification tasks. This paper aims to provide a contribution to this setting. In particular, we start by creating a new dataset called EURO-Coin, which consists of images showing the side of coins with reliefs and is designed to facilitate the training and testing of AI models for euro coin classification. Then, we propose two approaches that leverage Convolutional Neural Networks and self-attention layers to classify the country and value of the coins. In our experiments, we obtain an accuracy of 86.9% for country classification and an accuracy of 96.4% for value classification. Finally, we conduct an ablation study to evaluate the impact of the preprocessing activities and attention layers in our approaches
A deep learning approach to classify country and value of modern coins
Full text linkThe use of Artificial Intelligence (AI) to preserve and promote cultural heritage has experienced significant growth in recent years. Among the various areas of cultural heritage, numismatics have emerged as a particularly promising field where we can develop AI solutions. Numismatics refers to the study of coins, tokens, paper money, and medals, which play a critical role in understanding human history and culture. However, there are still limited resources available to help researchers and collectors in the identification of coins. This is due to the vast number of coins in circulation, which presents a significant challenge in developing smart tools for classification tasks. This paper aims to provide a contribution to this setting. In particular, we start by creating a new dataset called EURO-Coin, which consists of images showing the side of coins with reliefs and is designed to facilitate the training and testing of AI models for euro coin classification. Then, we propose two approaches that leverage Convolutional Neural Networks and self-attention layers to classify the country and value of the coins. In our experiments, we obtain an accuracy of 86.9% for country classification and an accuracy of 96.4% for value classification. Finally, we conduct an ablation study to evaluate the impact of the preprocessing activities and attention layers in our approache
Exploring the ability of emerging large language models to detect cyberbullying in social posts through new prompt-based classification approaches
No full textThe spread of new social networks in recent years, especially among adolescents, has increased the spread of social posts encouraging harmful behaviors, targeting people based on factors such as race, sex, or personal beliefs. This phenomenon makes it necessary to define intelligent tools capable of efficiently analyzing social media content. Recent Large Language Models (LLMs) have demonstrated advanced text generation and comprehension capabilities, making them efficient tools for identifying harmful posts. In this paper, we perform a large-scale evaluation of 20 generative LLMs in detecting cyberbullying phenomena in real social media posts through a new ad-hoc prompt Machine Learning approach (Prompt-based ML). We evaluate LLMs on binary and multiclass classification tasks on thousands of real posts from X, Facebook, and Reddit, and also compare their performance with 24 machine learning and natural language processing models. Specifically, the comparison analysis aims to understand the cyberbullying discrimination capability of LLMs with respect to traditional models, and the obtained findings to select suitable models for identifying harmful content on social network platforms. Furthermore, we provide an evaluation of the clarity, coherence, and relevance of the explanations provided by LLMs downstream of the identification of cyberbullying in social posts involving three domain experts. Experimental results highlight high performances of LLMs, particularly Claude 3.0 and Mistral family models, in identifying different types of cyberbullying. The domain expert evaluation of explainability showed that LLMs belonging to the Claude and Mistral families had better scores for clarity, coherence and relevance in their explanations compared to other models
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
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
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
Evaluating password strength based on information spread on social networks: A combined approach relying on data reconstruction and generative models
No full textEnsuring the security of personal accounts has become a key concern due to the widespread password attack techniques. Although passwords are the primary defense against unauthorized access, the practice of reusing easy-to-remember passwords increases security risks for people. Traditional methods for evaluating password strength are often insufficient since they overlook the public personal information that users frequently share on social networks. In addition, while users tend to limit access to their data on single profiles, personal data is often unintentionally shared across multiple profiles, exposing users to password threats. In this paper, we present an extension of a data reconstruction tool, namely SODA ADVANCE, which incorporates a new module to evaluate password strength based on publicly available data across multiple social networks. It relies on a new metric to provide a comprehensive evaluation of password strength. Moreover, we investigate the capabilities and risks associated with emerging Large Language Models (LLMs) in evaluating and generating passwords, respectively. Specifically, by exploiting the proliferation of LLMs, it has been possible to interact with many LLMs through Automated Template Learning methodologies. Experimental evaluations, performed with 100 real users, demonstrate the effectiveness of LLMs in generating strong passwords with respect to data associated with users’ profiles. Furthermore, LLMs have proved to be effective also in evaluation tasks, but the combined usage of LLMs and SODA ADVANCE guaranteed better classifications up to more than 10% in terms of F1-score
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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