Nowadays, technology makes it possible to admire objects and artworks exhibited all over the world remotely. We have been able to appreciate this convenience even more in the last period, in which the pandemic has forced us into our homes for a long time. However, visiting art sites in person remains a truly unique experience. Even during on-site visits, technology can help make them much more satisfactory, by assisting visitors during the fruition of cultural and artistic resources. To this aim, it is necessary to monitor the active user for acquiring information about their behavior. We, therefore, need systems able to monitor and analyze visitor behavior. The literature proposes several techniques for the timing and tracking of museum visitors. In this article, we propose a novel approach to indoor tracking that can represent a promising and non-expensive solution for some of the critical issues that remain. In particular, the system we propose relies on low-cost equipment (i.e., simple badges and off-the-shelf RGB cameras) and harnesses one of the most recent deep neural networks (i.e., Faster R-CNN) for detecting specific objects in an image or a video sequence with high accuracy. An experimental evaluation performed in a real scenario, namely, the “Exhibition of Fake Art” at Roma Tre University, allowed us to test our system on site. The collected data has proven to be accurate and helpful for gathering insightful information on visitor behavior.

In response to queries asked to a statistical database, the query system should avoid releasing summary statistics that could lead to the disclosure of confidential individual data. Attacks to the security of a statistical database may be direct or indirect and, in order to repel them, the query system should audit queries by controlling the amount of information released by their responses. This paper focuses on sum-queries with a response variable of nonnegative real type and proposes a compact representation of answered sum-queries, called an information model in “normal form,” which allows the query system to decide whether the value of a new sum-query can or cannot be safely answered. If it cannot, then the query system will issue the range of feasible values of the new sum-query consistent with previously answered sum-queries. Both the management of the information model and the answering procedure require solving linear-programming problems and, since standard linear-programming algorithms are not polynomially bounded (despite their good performances in practice), effective procedures that make a parsimonious use of them are stated for the general case. Moreover, in the special case that the information model is “graphical.” It is shown that the answering procedure can be implemented in polynomial time.

In this paper we propose a simple algorithm called CliqueMinTriang for computing a minimal triangulation of a graph. If $F$ is the set of edges that is added to $G$ to make it a complete graph $K_n$ then the asymptotic complexity of CliqueMinTriang is $O\left(\left|F\right|({\delta }^2+\left|F\right|)\right)$ where $\delta$ is the degree of the subgraph of $K_n$ induced by $F$. Therefore our algorithm performs well when $G$ is a dense graph. We also show how to exploit the existing minimal triangulation techniques in conjunction with CliqueMinTriang to efficiently find a minimal triangulation of nondense graphs. Finally we show how the algorithm can be adapted to perform a backward stepwise selection of decomposable Markov networks; the resulting procedure has the same time complexity as that of existing similar algorithms.