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In this paper we present a novel algorithm to synthesize an optimal decision tree from OR-decision tables, an extension of standard decision tables, complete with the formal proof of optimality and computational cost analysis. As many problems which require to recognize particular patterns can be modeled with this formalism, we select two common binary image processing algorithms, namely connected components labeling and thinning, to show how these can be represented with decision tables, and the benefits of their implementation as optimal decision trees in terms of reduced memory accesses. Experiments are reported, to show the computational time improvements over state of the art implementations.

The number of physical objects connected to the Internet constantly grows and a common thought says the IoT scenario will change the way we live and work. Since IoT technologies have the potential to be pervasive in almost every aspect of a human life, in this paper, we deeply analyze the IoT scenario. First, we describe IoT in simple terms and then we investigate what current technologies can achieve. Our analysis shows four major issues that may limit the use of IoT (i.e., interoperability, security, privacy, and business models) and it highlights possible solutions to solve these problems. Finally, we provide a simulation analysis that emphasizes issues and suggests practical research directions.

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Social media platforms contain interesting information that can be used to directly measure people' feelings and, thanks to the use of communication technologies, also to geographically locate these feelings. Unfortunately, the understanding is not as easy as one may think. Indeed, the large volume of data makes the manual approach impractical and the diversity of language combined with the brevity of the texts makes the automatic approach quite complicated. In this paper, we consider the gamification approach to sentimentally classify tweets and we propose TSentiment, a game with a purpose that uses human beings to classify the polarity of tweets (e.g., positive, negative, neutral) and their sentiment (e.g., joy, surprise, sadness, etc.). We created a dataset of more than 65,000 tweets, we developed a Web-based game and we asked students to play the game. Obtained results showed that the game approach was well accepted and thus it can be useful in scenarios where the identification of people' feelings may bring benefits to decision making processes

Museums are embracing social technologies in an attempt to broaden their audience and to engage people. Although social communication seems an easy task, media managers know how hard it is to reach millions of people with a simple message. Indeed, millions of posts are competing every day to get visibility in terms of likes and shares and very little research focused on museums communication to identify best practices. In this article, we focus on Twitter and we propose a novel method that exploits interpretable machine learning techniques to: (a) predict whether a tweet will likely be appreciated by Twitter users or not; (b) present simple suggestions that will help to enhance the message and increase the probability of its success. Using a real-world dataset of around 40,000 tweets written by 23 world famous museums, we show that our proposed method allows identifying tweet features that are more likely to influence the tweet success.