86,857 research outputs found
Outdoor Places of Interest Recognition Using WiFi Fingerprints
No full textThe growing interest in concepts such as smart cities and smart mobility is giving more and more importance to place of interest (POI) information, which proves to be crucial in providing efficient and tailored location-based services (LBSs). Though plenty of solutions exist for recognizing indoor places, the literature lacks of approaches aimed at recognizing big outdoor places without the GPS employment. Even if GPS-based solutions assure great accuracy, they have a strong request in terms of energy necessary to achieve such result. As a consequence, if LBSs are thought on the move (e.g., mobile devices such as smartphones are used) energy consumption is a key constraint. This paper proposes a POI recognition algorithm called enhanced location recognition algorithm for automatic check-in applications (E-LRACI). It is an evolution of LRACI (Location Recognition Algorithm for automatic Check-In applications, originally reported in [I. Bisio, F. Lavagetto, M. Marchese, and A. Sciarrone, ''GPS/HPS-andwifi fingerprint-based location recognition for check-in applications over smartphones in cloud-based LBSS,'' IEEE Transactions on Multimedia, vol. 15, no. 4, pp. 858-869, Jun. 2013]) which aims at recognizing big outdoor places by only exploiting radio beacons emitted by WiFi access points. In terms of contributions, this paper first, proposes a novel fingerprint algorithm; second, solves the problem of big outdoor POI recognition without using GPS by leveraging the concept of spot; and third, compares the results obtained by E-LRACI and other reference works both in terms of recognition accuracy and computational complexity. The obtained numerical results, carried out on real data (acquired with Android-based smartphones), prove that E-LRACI provides the best results since it is able to guarantee the highest accuracy (95% versus, at most, 89%) at a lowest computational complexity with respect to the existing POI recognition algorithms
Anatomy and Empirical Evaluation of an Adaptive Web-Based Information Filtering System
No full textA case study in adaptive information filtering systems for the Web is presented. The described system comprises two main modules, named HUMOS and WIFS. HUMOS is a user modeling system based on stereotypes. It builds and maintains long term models of individual Internet users, representing their information needs. The user model is structured as a frame containing informative words, enhanced with semantic networks. The proposed machine learning approach for the user modeling process is based on the use of an artificial neural network for stereotype assignments. WIFS is a content-based information filtering module, capable of selecting html/text documents on computer science collected from the Web according to the interests of the user. It has been created for the very purpose of the structure of the user model utilized by HUMOS. Currently, this system acts as an adaptive interface to the Web search engine ALTA VISTATM. An empirical evaluation of the system has been made in experimental settings. The experiments focused on the evaluation, by means of a non-parametric statistics approach, of the added value in terms of system performance given by the user modeling component; it also focused on the evaluation of the usability and user acceptance of the system. The results of the experiments are satisfactory and support the choice of a user model-based approach to information filtering on the Web
I servizi di pagamento tra PSD2 e GDPR: Open Banking e conseguenze per la clientela.
No full textIl contributo tratta il tema dei servizi di pagamento alla luce della PSD2 e del GDP
K-OpenAnswer: a simulation environment to analyze the dynamics of massive open online courses in smart cities
No full textThe smartness of a city is given by the technologies it put to use, and more than that, by the people empowered by such technologies; it is worth thinking about how people can be trained to be empowered by smart technologies, and how cities can become “educational.” So, while sustainability and technology solutions for smart cities are strategic challenges, one of these is surely distance education and training. In this field, the Web offers many opportunities, such as the e-learning platforms where students can learn, according to their own needs and pace. The massive open online courses (MOOCs) are particular distance learning platforms, generally offering, so far, free courses on a huge amount of topics, and characterized by a (potentially) very high number of enrollments. In a MOOC, a teacher, or tutor, has a hard life when trying to follow and manage with the learning processes of thousands of students. In particular, assessment can be managed almost exclusively by letting the student answer questions in closed answers tests. This strategy has some didactic limits, while a valid alternative is to use peer assessment (PA) over more articulated assessment activities (e.g., open-ended questions). PA makes students grade their peers’ answers, and provides learners with significant advantages, such as refining their knowledge of the subject matter, and developing their meta-cognitive skills. In this work, we present a software platform called K-OpenAnswer, which helps teachers to simulate the dynamic of a MOOC where PA is used. The system uses a machine learning technique, based on a modified version of the K-NN algorithm, and provides teachers with a statistical environment by which they can monitor the evolving dynamic of a simulated MOOC, according to the techniques we use to implement PA. An experimental evaluation is presented that highlights the advantages of using the system as a valid tool for the study of real MOOCs
Monitoring Massive Open Online Courses (MOOC) During the Covid-19 Pandemic
No full textThe last two years have been characterized by an exponential growth in the use of the Internet as a working and learning tool, due to the “Covid-19” pandemic. The increase in smart working and distance learning have been some of the most striking effects. Cities have become more eco-sustainable: less pollution and better life quality. In this work we present a brief review of some data science platforms, which are useful to monitor the learning processes that take place in Massive Open Online Courses. Through these tools, teachers could find out useful information about the learning processes, by extracting it from the (big) data produced by students’ activities and stored in log files. To show the usefulness of such tools, we propose a very simple case study, showing how to extract strategic information from the log database produced by a course delivered via Moodle platform. The results of this case study strengthen our hypothesis on the utility of a data science approach for monitoring learning processes, especially in MOOCs
Interventi di Maurizio Ambrosini, Pietro Fantozzi, Enzo Pace, Francesco Ramella, Rocco Sciarrone, Lia Tirabeni, Mara Tognetti
No full textChi, docente e/o ricercatore, usa il sapere sociologico rispondendo a richieste d’intervento professionale, intreccia nel suo operare relazioni che dialetticamente mettono alla prova e sviluppano la specificità sociologica delle sue competenze. Vada o meno l’intervento a buon fine, spesso molto dipende dalle modalità con cui si attua il processo interattivo e cooperativo: nelle
due direzioni, come di seguito esposto
A social network-based teacher model to support course construction
No full textOn line education is a student centred activity, and most of the research in this field focuses on students; yet the quality of teaching is undoubtedly the basic ingredient for a successful learning. In particular, fostering new forms of collaboration between students and teachers, i.e. pursuing co-learning aspects of e-learning, probably needs giving teachers new means of collaboration, also among themselves. In this paper, we tackle the aim of providing the teacher with social collaboration tools, to support the process of course construction. Such a process comprises several distinct steps, from concept mapping, through selection of suitable learning material, to the final stages of delivery in a Learning Management System. It is an heavy process, through which teachers have to spend a lot of time to build or to retrieve the right learning material from local databases or from specialized repositories on the web. The support we foresee should exploit the knowledge of the whole teaching community, in which the teacher acts, to help her/him in doing the above described job. By "knowledge" we mean basically a representation of the ways of usage of learning materials, by the teachers in the community for their courses. To start on a solid footing, here we address the topic of modeling the teacher. The model we define aims to give teachers a personalized support, encompassing consideration for their own pedagogy, teaching styles, and teaching experience during course creation. It is deemed to consider all those issues in a dynamic way and to guide the teacher towards the best didactic choices
A Hybrid Architecture for the Recognition of User Interests during Hypermedia Navigation
No full textI non performing loan: un quadro d'insieme
No full textIl contributo intende tracciare le linee introduttive del fenomeno degli NPL da un punto di vista giuridic
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