Drug repurposing is a highly active research area, aiming at finding novel uses for drugs that have been previously developed for other therapeutic purposes. Despite the flourishing of methodologies, success is still partial, and different approaches offer, each, peculiar advantages. In this composite landscape, we present a novel methodology focusing on an efficient mathematical procedure based on gene similarity scores and biased random walks which rely on robust drug-gene-disease association data sets. The recommendation mechanism is further unveiled by means of the Markov chain underlying the random walk process, hence providing explainability about how findings are suggested. Performances evaluation and the analysis of a case study on rheumatoid arthritis show that our approach is accurate in providing useful recommendations and is computationally efficient, compared to the state of the art of drug repurposing approaches.

Individual tracking of museum visitors based on portable radio beacons, an asset for behavioural analyses and comfort/performance improvements, is seeing increasing diffusion. Conceptually, this approach enables room-level localisation based on a network of small antennas (thus, without invasive modification of the existent structures). The antennas measure the intensity (RSSi) of self-advertising signals broadcasted by beacons individually assigned to the visitors. The signal intensity provides a proxy for the distance to the antennas and thus indicative positioning. However, RSSi signals are well-known to be noisy, even in ideal conditions (high antenna density, absence of obstacles, absence of crowd, ...). In this contribution, we present a method to perform accurate RSSi-based visitor tracking when the density of antennas is relatively low, e.g. due to technical constraints imposed by historic buildings. We combine an ensemble of "simple" localisers, trained based on ground-truth, with an encoding of the museum topology in terms of a total-coloured graph. This turns the localisation problem into a cascade process, from large to small scales, in space and in time. Our use case is visitors tracking in Galleria Borghese, Rome (Italy), for which our method manages >96% localisation accuracy, significantly improving on our previous work (J. Comput. Sci. 101357, 2021).

We address the problem of user fast revocation in the lattice-based Ciphertext Policy Attribute-Based Encryption (CP-ABE) by extending the scheme originally introduced by Zhang and Zhang [Zhang J, Zhang Z. A ciphertext policy attribute-based encryption scheme without pairings . In: International Conference on Information Security and Cryptology. Springer; 2011. p. 324–40. doi: https://doi.org/10.1007/978-3-642-34704-7_23 .]. While a lot of work exists on the construction of revocable schemes for CP-ABE based on pairings, works based on lattices are not so common, and – to the best of our knowledge – we introduce the first server-aided revocation scheme in a lattice-based CP-ABE scheme, hence being embedded in a post-quantum secure environment. In particular, we rely on semi-trusted “mediators” to provide a multi-step decryption capable of handling mediation without re-encryption. We comment on the scheme and its application, and we provide performance experiments on a prototype implementation in the Attribute-Based Encryption spin-off library of Palisade to evaluate the overhead compared with the original scheme.