Nowadays, the role of a tutor is more important than ever to prevent students dropout and improve their academic performance. This work proposes a data-driven system to extract relevant information hidden in the student academic data and, thus, help tutors to offer their pupils a more proactive personal guidance. In particular, our system, based on machine learning techniques, makes predictions of dropout intention and courses grades of students, as well as personalized course recommendations. Moreover, we present different visualizations which help in the interpretation of the results. In the experimental validation, we show that the system obtains promising results with data from the degree studies in Law, Computer Science and Mathematics of the Universitat de Barcelona.

Nowadays, there are a plethora of services that are provided and paid for online, such as video streaming subscriptions, car-share, vehicle parking, purchasing tickets for events, etc. Online services usually issue tokens that are directly related to the identities of their users after they sign up to a platform; users need to authenticate themselves by using the same credentials each time they use the service. Likewise, when using in-person services, such as going to a concert, after paying for this service, the user usually receives a ticket, which proves that he/she has the right to use that service. In both scenarios, the main concerns surround the centralization of these systems and that they do not ensure customers’ privacy. The involved service providers are trusted third parties—authorities that offer services and handle private data about users. In this paper, we designed and implemented FORT, a decentralized system that allows customers to prove their rights to use specific services (either online or in-person) without revealing sensitive information. To achieve decentralization, we proposed a solution where all of the data are handled by a blockchain. We describe and uniquely identify users’ rights using non-fungible tokens (NFTs), and possession of these rights is demonstrated by using zero-knowledge proofs—cryptographic primitives that allow us to guarantee customers’ privacy. Furthermore, we provide benchmarks of FORT, which show that our protocol is efficient enough to be used in devices with low computing resources, such as smartphones or smartwatches, which are devices commonly used in our use case scenario.

This paper analyses the adoption of new information and communication technologies (ICTs) by Spanish journalists specialising in science. Applying an ethnographic research model, this study was based on a wide sample of professionals, aiming to evaluate the extent by which science journalists have adopted the new media and changed the way they use information sources. In addition, interviewees were asked whether in their opinion the Web 2.0 has had an impact on the quality of the news. The integration of formats certainly implies a few issues for today’s newsrooms. Finally, with the purpose of improving the practice of science information dissemination, the authors put forward a few proposals, namely: Increasing the training of Spanish science journalists in the field of new technologies; Emphasising the accuracy of the information and the validation of sources; Rethinking the mandates and the tasks of information professionals.

TFHE is a practical fully homomorphic encryption scheme (FHE) capable of computing any boolean gate or non-linear function. The scheme was originally designed to work for the single key setting. To implement realistic application scenarios, it is necessary to extend it to handle multiple users. In this paper, we introduce a new approach to generate TFHE bootstrapping keys for (predefined) multiple users. Hence, a fixed number of users can enjoy the same level of efficiency as in the single key setting, keeping their individual input privacy. Our construction relies on a novel algorithm called homomorphic indicator, which can be of independent interest. We provide a detailed analysis of the noise growth and a set of secure parameters suitable to be used in practice. Moreover, we compare the complexity of our technique with other state-of-the-art constructions and show which method performs better depending on the parameter sets. We also provide a prototype implementation of our technique. To the best of our knowledge, this is the first implementation of TFHE in the multiparty setting.