Sergio Rabellino

Peer-to-Peer Desktop Grids in the Real World: The ShareGrid Project

ShareGrid is a peer-to-peer desktop grid aimed at satisfying the computing needs of the small research laboratories located in the Piedmont area in Northern Italy. Share- Grid adopts a cooperative approach, in which each participant allows the other ones to use his/her own resources on a reciprocity basis. ShareGrid is based on the OurGrid middleware, that provides a set of mechanisms enabling participating entities to quickly, fairly, and securely share their resources. In this paper we report our experience in designing, deploying, and using ShareGrid, and we describe the applications using it, as well as the lessons we learned, the problems that still remain open, and some possible solutions to them.

Automated Assessment in Mathematics

Assessment of learning and assessment for learning are at the core of the research on new teaching strategies involving the use of new technologies recently performed by University of Turin. The practice of automated assessment in Mathematics using the grading system Maple T.A. Has been introduced in many undergraduate scientific courses and, after the initial success, it has been diffused in high-schools through several projects aimed to improve Maths teaching and learning. The following paper is intended to describe the effectiveness of automated assessment as a learning tool, the strength of Maple T.A. For grading Mathematics and its integration in a learning content management system, and the results obtained at University and in high-schools.

OCCAM: a flexible, multi-purpose and extendable HPC cluster

The Open Computing Cluster for Advanced data Manipulation (OCCAM) is a multi-purpose flexible HPC cluster designed and operated by a collaboration between the University of Torino and the Sezione di Torino of the Istituto Nazionale di Fisica Nucleare. It is aimed at providing a flexible, reconfigurable and extendable infrastructure to cater to a wide range of different scientific computing use cases, including ones from solid-state chemistry, high-energy physics, computer science, big data analytics, computational biology, genomics and many others. Furthermore, it will serve as a platform for R&D activities on computational technologies themselves, with topics ranging from GPU acceleration to Cloud Computing technologies. A heterogeneous and reconfigurable system like this poses a number of challenges related to the frequency at which heterogeneous hardware resources might change their availability and shareability status, which in turn affect methods and means to allocate, manage, optimize, bill, monitor VMs, containers, virtual farms, jobs, interactive bare-metal sessions, etc. This work describes some of the use cases that prompted the design and construction of the HPC cluster, its architecture and resource provisioning model, along with a first characterization of its performance by some synthetic benchmark tools and a few realistic use-case tests.

HPC4AI: an AI-on-demand federated platform endeavour

In April 2018, under the auspices of the POR-FESR 2014-2020 program of Italian Piedmont Region, the Turins Centre on High-Performance Computing for Artificial Intelligence (HPC4AI) was funded with a capital investment of 4.5M€ and it began its deployment. HPC4AI aims to facilitate scientific research and engineering in the areas of Artificial Intelligence and Big Data Analytics. HPC4AI will specifically focus on methods for the on-demand provisioning of AI and BDA Cloud services to the regional and national industrial community, which includes the large regional ecosystem of Small-Medium Enterprises (SMEs) active in many different sectors such as automotive, aerospace, mechatronics, manufacturing, health and agrifood.

Ontology Development for Competence Assessment in Virtual Communities of Practice

This paper presents an ontological model for defining competency paths in STEM education, designed for the implementation of an adaptive system integrated in virtual communities. The model is applied for clustering materials for automatic assessment and the results are discussed.

Alignment of Content, Prerequisites and Educational Objectives: Towards Automated Mapping of Digital Learning Resources

The emergence of Technology Enhanced Learning environments has led to the continual growth of the availability of digital educational resources. In this paper, the potential of enabling their reuse into student-centric services – such as recommender systems or adaptive tutoring tools – is discussed through the proposal and comparison of procedures for automatically detecting the mutual relatedness among learning objects. Since the choice of the similarity measure is fundamental for clustering digital materials, this paper addresses the investigation on two distinct approaches: the content-based semantic similarity, compared to the closeness measure on natural language descriptions of metadata – namely prerequisites and educational objectives. The analysis is conducted on a collection of mathematical problems, equipped with metadata which facilitate their retrieval in Virtual Learning Environments, created by Secondary School teachers with the support of University experts. Natural Language Processing techniques are exploited for extracting relevant information from the metadata, while the developments in the emergent field of Mathematical Language Processing are proposed for the treatment of mathematical expressions included in the resources. The distinct similarity measures presented are examined considering the compared results, and their correlation is evaluated. This study is intended to be the first step towards the definition of a model for structuring shared materials available in disciplinary repositories of virtual communities. This model will be used for implementing a system for the delivery of learning objects trajectories on a digital map automatically generated. The system’s efficacy will be tested through its integration to a Learning Management System hosting secondary school classrooms’ courses. The research is part of a PhD in Pure and Applied Mathematics in apprenticeship, conducted in partnership with leading providers of software based on Computer Algebra System engine.

Self-Paced Approach in Synergistic Model for Supporting and Testing Students

This paper shows the model developed by the University of Turin to support students that must face the transition from the last year of secondary school to the first year of University. Integrations that are specifically designed for Learning Management Systems help sustain three effective actions conducted in synergy: increase students’ awareness in the choice of the future course of study, support them in taking the admission tests and the first-year exams, allow the autonomous administration of admission tests led by the University. The methodological strategies adopted are presented and discussed based on the analysis of the data of the years 2015 and 2016.

ADVANCED E-LEARNING FOR IT-ARMY OFFICERS THROUGH VIRTUAL LEARNING ENVIRONMENTS

In this work we will present and discuss the joint experience of the IT-Army Education and Training Command and School of Applied Military Studies of Turin and the University of Turin, that worked together to designed an advanced e-learning path. This was made possible by the use of digital methodologies and integrated virtual learning environments, with the aim of supporting in the best possible way the military officers training, which has to be continuous, highly specialized, multidisciplinary, flexible, and strongly internationalized.

Open Platform of self-paced MOOCs for the continual improvement of Academic Guidance and Knowledge Strengthening in Tertiary Education

In the academic year 2014/2015, University of XXX started the Project Orient@mente , aimed to support students in the transition from high school to university. Several Massive Open Online Courses have been developed to support three main actions: guidance to the University offer, automated self-testing of basic knowledge, self-paced review of fundamental disciplinary concepts learned in high school; all of them are useful to help students successfully attend scientific courses of the first year of University. A key feature of the Project is the continuous open-access to the platform. Contents are built according to educational models grown thanks to the experience and the research in e-learning carried out by the University, especially in the use of an accessible learning management system integrated with an advanced computing environment, an automated assessment system and a web conference system to enhance teaching and learning. In this paper, the adopted methodologies are discussed, the obtained results are presented and future developments are proposed in light of relevant data collected from the platform usage and feedback received by users.

Preface from the organizing chairs

The University of Turin is one of the oldest and largest universities in Italy (founded in 1404), and has more than 2000 lecturers and professors. The University of Turin can count many notable alumni and faculty, including, in formal and physical sciences: Joseph-Louis Lagrange (born Giuseppe Luigi Lagrangia), Augustin Cauchy, Giuseppe Peano, Galileo Ferraris; in medicine: Salvador Luria, Renato Dulbecco and Rita Levi-Montalcini (Nobel Prizes); in philosophy and literature: Erasmus of Rotterdam, Primo Levi, Cesare Pavese, Umberto Eco. The Computer Science department was founded in 1971 and today has 37 professors (12 full, 23 associate), 38 permanent researchers, 21 research fellows, and about 40 PhD students. Their scientific activity covers a wide range of areas, including concurrency theory, parallel, distributed and high performance computing, and clouds.