Toward reliable machine learning with Congruity: a quality measure based on formal concept analysis.

The spreading of machine learning (ML) and deep learning (DL) methods in different and critical application domains, like medicine and healthcare, introduces many opportunities but raises risks and opens ethical issues, mainly attaining to the lack of transparency. This contribution deals with the lack of transparency of ML and DL models focusing on the lack of trust in predictions and decisions generated. In this sense, this paper establishes a measure, namely Congruity, to provide information about the reliability of ML/DL model results. Congruity is defined by the lattice extracted through the formal concept analysis built on the training data. It measures how much the incoming data items are close to the ones used at the training stage of the ML and DL models. The general idea is that the reliability of trained model results is highly correlated with the similarity of input data and the training set. The objective of the paper is to demonstrate the correlation between the Congruity and the well-known Accuracy of the whole ML/DL model. Experimental results reveal that the value of correlation between Congruity and Accuracy of ML model is greater than 80% by varying ML models.

Covid-19 sentiments in smart cities: The role of technology anxiety before and during the pandemic.

The spread of Covid-19 profoundly changed citizens' daily lives due to the introduction of new modes of work and access to services based on smart technologies. Although the relevance of new technologies as strategic levers for crisis resolution has been widely debated before the pandemic, especially in the smart cities' context, how individuals have agreed to include the technological changes dictated by the pandemic in their daily interactions remains an open question. This paper aims at detecting citizens' sentiment toward technology before and after the emergence of the Covid-19 pandemic using Fuzzy Formal Concept Analysis (FFCA) to analyze a large corpus of tweets. Specifically, citizens' attitudes in five cities (Berlin, Dublin, London, Milan, and Madrid) were explored to extract and classify the key topics related to the degree of confidence, familiarity and approval of new technologies. The results shed light on the complex technology acceptance process and help managers identify the potential negative effects of smart technologies. In this way, the study enhances scholars' and practitioners' understanding of the strategies for enabling the use of technology within smart cities to manage the transformations introduced by the health emergency and guide citizens' behaviour.

Patterns for Visual Management in Industry 4.0.

The technologies of Industry 4.0 provide an opportunity to improve the effectiveness of Visual Management in manufacturing. The opportunity of improvement is twofold. From one side, Visual Management theory and practice can inspire the design of new software tools suitable for Industry 4.0; on the other side, the technology of Industry 4.0 can be used to increase the effectiveness of visual software tools. The paper first explores how the theoretical result on Visual Management can be used as a guideline to improve human-computer interaction, then a methodology is proposed for the design of visual patterns for manufacturing. Four visual patterns are presented that contribute to the solution of problems frequently encountered in discrete manufacturing industries; these patterns help to solve planning and control problems thus providing support to various management functions. Positive implications of this research concern people engagement and empowerment as well as improved problem solving, decision-making and management of manufacturing processes.

A Novel Method for Validating Multi-Classifiers. A Case Study for ICF-Based Health Status Classification.

In this paper, we propose a novel method for the validation of a multi-classification model according to the intended use and aim of a device for health status classification and the clinical needs of the practitioners involved.