arXiv subjects
Publications and source records attributed to Paolo Torroni.
Deep learning is bringing remarkable contributions to the field of argumentation mining, but the existing approaches still need to fill the gap toward performing advanced reasoning tasks. In this position paper, we posit that neural-symbolic and statistical relational learning could play a crucial role in the integration of symbolic and sub-symbolic methods to achieve this goal.
Terms of service of on-line platforms too often contain clauses that are potentially unfair to the consumer. We present an experimental study where machine learning is employed to automatically detect such potentially unfair clauses. Results show that the proposed system could provide a valuable tool for lawyers and consumers alike.
The Priority Inheritance Protocol (PIP) is arguably the best-known protocol for resource sharing under real-time constraints. Its importance in modern applications is undisputed. Nevertheless, because jobs may be blocked under PIP for a variety of reasons, determining a job's maximum blocking time could be difficult, and thus far no exact method has been proposed that does it. Existing analysis methods are inefficient, inaccurate, and of limited applicability. This article proposes a new characterization of the problem, thus allowing a polynomial method for bounding the blocking time, and an exact, optimally efficient method for blocking time computation under priority inheritance that have a general applicability.