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Concurrency is a complex to learn topic that is becoming more and more relevant, such that many undergraduate Computer Science curricula are introducing it in introductory programming courses. This paper investigates the combined use of Sonic Pi and Team-Based Learning to mitigate the difficulties in early exposure to concurrency. Sonic Pi, a domain-specific music language, provides great support for “playing” with concurrency and “hearing” common problems such as data races and lack of synchronization among different concurrent threads. More specifically, the paper focuses on students’ misconceptions regarding concurrency in Sonic Pi, and compares them to those arising in traditional concurrent programming languages. In addition, it preliminarily explores knowledge transfer from Sonic Pi to C/C++. The approach has been applied in two teaching experiments with undergraduate students in our University involving 184 participants. Our investigations bring out the need to address misconceptions through targeted interventions for a clear understanding of concurrent programming concepts. Sonic Pi’s simplified abstraction and domain-specific flavor has demonstrated to be effective, especially for first-year students.

In this paper, we address the problem of designing new formats of computer science orientation activities to be offered during high school students internships in Computer Science Bachelor degrees. In order to cover a wide range of computer science topics as well to deal with soft skills and gender gap issues, we propose a teamwork format, called smart rogaining, that offer engaging introductory activities to prospective students in a series of checkpoints dislocated along the different stages of a rogaine. The format is supported by a smart mobile and web application. Our proposal is aimed at stimulating the interest of participants in different areas of computer science and at improving digital and soft skills of participants and, as a side effect, of staff members (instructors and university students). In the paper, we introduce the proposed format and discuss our experience in the editions organized at the University of Genoa before the COVID-19 pandemic (2019 and 2020 waves).

Abstract Background Pathogenesis of inflammatory diseases can be tracked by studying the causality relationships among the factors contributing to its development. We could, for instance, hypothesize on the connections of the pathogenesis outcomes to the observed conditions. And to prove such causal hypotheses we would need to have the full understanding of the causal relationships, and we would have to provide all the necessary evidences to support our claims. In practice, however, we might not possess all the background knowledge on the causality relationships, and we might be unable to collect all the evidence to prove our hypotheses. Results In this work we propose a methodology for the translation of biological knowledge on causality relationships of biological processes and their effects on conditions to a computational framework for hypothesis testing. The methodology consists of two main points: hypothesis graph construction from the formalization of the background knowledge on causality relationships, and confidence measurement in a causality hypothesis as a normalized weighted path computation in the hypothesis graph. In this framework, we can simulate collection of evidences and assess confidence in a causality hypothesis by measuring it proportionally to the amount of available knowledge and collected evidences. Conclusions We evaluate our methodology on a hypothesis graph that represents both contributing factors which may cause cartilage degradation and the factors which might be caused by the cartilage degradation during osteoarthritis. Hypothesis graph construction has proven to be robust to the addition of potentially contradictory information on the simultaneously positive and negative effects. The obtained confidence measures for the specific causality hypotheses have been validated by our domain experts, and, correspond closely to their subjective assessments of confidences in investigated hypotheses. Overall, our methodology for a shared hypothesis testing framework exhibits important properties that researchers will find useful in literature review for their experimental studies, planning and prioritizing evidence collection acquisition procedures, and testing their hypotheses with different depths of knowledge on causal dependencies of biological processes and their effects on the observed conditions.

The structure of an XML document can be optionally specified by means of XML Schema, thus enabling the exploitation of structural information for efficient document handling. Upon schema evolution, or when exchanging documents among different collections exploiting related but not identical schemas, the need may arise of adapting a document, known to be valid for a given schema S, to a target schema S'. The adaptation may require knowledge of the element semantics and cannot always be automatically derived. In this paper, we present an automata-based method for the static analysis of user-defined XML document adaptations, expressed as sequences of XQuery Update update primitives. The key feature of the method is the use of an automatic inference method for extracting the type, expressed as a Hedge Automaton, of a sequence of document updates. The type is computed starting from the original schema S and from rewriting rules that formally define the operational semantics of a sequence of document updates. Type inclusion can then be used as conformance test w.r.t. the type extracted from the target schema S'.