1,721,006 research outputs found
Automatic Detection of Sensitive Data Using Transformer- Based Classifiers
No full textThe General Data Protection Regulation (GDPR) has allowed EU citizens and residents to have more control over their personal data, simplifying the regulatory environment affecting international business and unifying and homogenising privacy legislation within the EU. This regulation affects all companies that process data of European residents regardless of the place in which they are processed and their registered office, providing for a strict discipline of data protection. These companies must comply with the GDPR and be aware of the content of the data they manage; this is especially important if they are holding sensitive data, that is, any information regarding racial or ethnic origin, political opinions, religious or philosophical beliefs, trade union membership, data relating to the sexual life or sexual orientation of the person, as well as data on physical and mental health. These classes of data are hardly structured, and most frequently they appear within a document such as an email message, a review or a post. It is extremely difficult to know if a company is in possession of sensitive data at the risk of not protecting them properly. The goal of the study described in this paper is to use Machine Learning, in particular the Transformer deep-learning model, to develop classifiers capable of detecting documents that are likely to include sensitive data. Additionally, we want the classifiers to recognize the particular type of sensitive topic with which they deal, in order for a company to have a better knowledge of the data they own. We expect to make the model described in this paper available as a web service, customized to private data of possible customers, or even in a free-to-use version based on the freely available data set we have built to train the classifiers
Catalogazione, ricerca e visualizzazione di oggetti didattici: note e considerazioni nello sviluppo di un progetto
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Mobility in Unsupervised Word Embeddings for Knowledge Extraction—The Scholars’ Trajectories across Research Topics
Full text linkIn the knowledge discovery field of the Big Data domain the analysis of geographic positioning and mobility information plays a key role. At the same time, in the Natural Language Processing (NLP) domain pre-trained models such as BERT and word embedding algorithms such as Word2Vec enabled a rich encoding of words that allows mapping textual data into points of an arbitrary multi-dimensional space, in which the notion of proximity reflects an association among terms or topics. The main contribution of this paper is to show how analytical tools, traditionally adopted to deal with geographic data to measure the mobility of an agent in a time interval, can also be effectively applied to extract knowledge in a semantic realm, such as a semantic space of words and topics, looking for latent trajectories that can benefit the properties of neural network latent representations. As a case study, the Scopus database was queried about works of highly cited researchers in recent years. On this basis, we performed a dynamic analysis, for measuring the Radius of Gyration as an index of the mobility of researchers across scientific topics. The semantic space is built from the automatic analysis of the paper abstracts of each author. In particular, we evaluated two different methodologies to build the semantic space and we found that Word2Vec embeddings perform better than the BERT ones for this task. Finally, The scholars’ trajectories show some latent properties of this model, which also represent new scientific contributions of this work. These properties include (i) the correlation between the scientific mobility and the achievement of scientific results, measured through the H-index; (ii) differences in the behavior of researchers working in different countries and subjects; and (iii) some interesting similarities between mobility patterns in this semantic realm and those typically observed in the case of human mobility
Purposive visual perception and co-operative behaviour: some issues for the design of physical agents
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