Ricerc@Sapienza

The identification and classification of bio-chemical substances are very important tasks in chemical, biological and forensic analysis. In this work we present a new strategy to improve the accuracy of the supervised classification of this type of data obtained from different analytical techniques that combine two processes: first, a dissimilarity representation of data and second, the selection of templates for the refinement of the representative samples in each class set. In order to evaluate the performance of our proposal, a comparative study between three approaches is presented.

The analysis of the shape and geometry of the human hand has long represented an at- tractive field of research to address the needs of digital image forensics. Over recent years, it has also turned out to be effective in biometrics, where several innovative research lines are pursued. Given the widespread diffusion of mobile and portable devices, the possibil- ity of checking the owner identity and controlling the access to the device by the hand image looks particularly attractive and less intrusive than other biometric traits.

Hand rehabilitation therapy is fundamental for post-stroke or post-surgery impairments. Traditional rehabilitation requires the presence of a therapist for executing and controlling therapy: this implies high costs, stress for the patient, and subjective evaluation of the therapy effectiveness. Alternative approaches, based on mechanical and tracking-based gloves, have been recently proposed.

Peer-assessment entails, for students, a very beneficial learning activity, from a pedagogical point of view. The peer-evaluation can be performed over a variety of peer-produced resources, the principle being that the more articulated such resource is, the better. Here we focus, in particular, on the automated support to grading open answers, via a peer-evaluation-based approach, which is mediated by the (partial) grading work of the teacher, and produces a (partial, as well) automated grading. We propose to support such automated grading by means of a method based on the K-NN technique.

The Zone of Proximal Development (ZPD) theorized by Lev Vygotskij can be considered as one of the most interesting insights on the value of collaborative learning: it specifies the cognitive distance reachable by a student in learning activities, given that some support by teachers, and peers, is available. Group handling is certainly one of the main aspects to be reproduced in an online learning system. This paper aims to offer a twofold contribution.

Questions with answers are rarely used as e-learning assessment tools because of the resulting high workload for the teacher/tutor that should grade them, which can be mitigated by doing peer-assessment. In OpenAnswer we modeled peer-assessment as a Bayesian network connecting the sub-networks representing any participating student to the corresponding answers of her graded peers.

In this work we introduce the operations of unbounded and bounded prefix-suffix square reduction. We show that, in general, the time complexity of the unbounded prefix-suffix square reduction of a language increases by an n factor in comparison to the complexity of the given language. This factor is just the bound in the case of bounded prefix-suffix square reduction and is not necessary for regular languages. As a consequence, the class of regular languages is closed under unbounded and bounded prefix-suffix square reduction.

We propose a highly parallel and distributed multiset computing model having as its underlying structure an undirected graph whose nodes are processors, each endowed with a polarity and with a set of rules all of the same kind, one of increment, decrement or substitution. Processors communicate with each other via a protocol based on the compatibility between their polarization and the polarization of the data, as computed by a valuation mapping. We show that this model can simulate any multiset Turing machine.

Vulnerability due to inter-connectivity of multiple networks has been observed in many complex networks. Previous works mainly focused on robust network design and on recovery strategies after sporadic or massive failures in the case of complete knowledge of failure location. We focus on cascading failures involving the power grid and its communication network with consequent imprecision in damage assessment. We tackle the problem of mitigating the ongoing cascading failure and providing a recovery strategy.