Several contact-tracing app-based solutions have been proposed to alert app users who have previously encountered a Covid-19-positive user. In most countries, a statesponsored app has been made available to let citizens who install it on their phone monitor inter-personal contacts.The particular scenario considered in this work aims to support tracing within a university campus. For this, an alternative solution is proposed, based on a hybrid decentralized approach, that traces people who have been present in the same environment within some meaningful time interval. Thus, tracing goes beyond direct contact between persons. Our solution can also monitor crowd gathering on campus premises.The proposed Android app senses surrounding WiFi signals and uses them to obtain the user's absolute location. The app then securely sends an anonymized presence data object to the server. Thanks to data thus gathered, as soon as a user has reported herself as Covid-19-positive, all apps will be alerted by the server and receive anonymous data to determine whether their users happened to be in the same environment as the Covid-positive one.We believe our approach to be both effective, for it eschews weaknesses and limitations of Bluetooth-based solutions, and viable, for the experiments reported have proved WiFi sensingbased localization to be accurate enough.

Background The rapidly increasing biological literature is a key resource to automatically extract and gain knowledge concerning biological elements and their relations. Knowledge Networks are helpful tools in the context of biological knowledge discovery and modeling. Results We introduce a novel system called NETME, which, starting from a set of full-texts obtained from PubMed, through an easy-to-use web interface, interactively extracts biological elements from ontological databases and then synthesizes a network inferring relations among such elements. The results clearly show that our tool is capable of inferring comprehensive and reliable biological networks.

The inference of novel knowledge and new hypotheses from the current literature analysis is crucial in making new scientific discoveries. In bio-medicine, given the enormous amount of literature and knowledge bases available, the automatic gain of knowledge concerning relationships among biological elements, in the form of semantically related terms (or entities), is rising novel research challenges and corresponding applications. In this regard, we propose BioTAGME, a system that combines an entity-annotation framework based on Wikipedia corpus (i.e., TAGME tool) with a network-based inference methodology (i.e., DT-Hybrid). This integration aims to create an extensive Knowledge Graph modeling relations among biological terms and phrases extracted from titles and abstracts of papers available in PubMed. The framework consists of a back-end and a front-end. The back-end is entirely implemented in Scala and runs on top of a Spark cluster that distributes the computing effort among several machines. The front-end is released through the Laravel framework, connected with the Neo4j graph database to store the knowledge graph.