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Suche nach „[F.] [Persia]“ hat 3 Publikationen gefunden
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    DigitalF: Europan Campus Rottal-Inn

    Beitrag (Sammelband oder Tagungsband)

    F. Persia, D. D'Auria, Mouzhi Ge

    Improving Learning System Performance with Multimedia Semantics

    2020 IEEE 14th International Conference on Semantic Computing (ICSC)

    2020

    DOI: 10.1109/ICSC.2020.00050

    Abstract anzeigen

    Nowadays, different new learning methodologies have been proposed to achieve effective learning in University education. One of the most promising methodologies for teaching computer science is multimedia-based education. In order to empower the performance within the online learning platforms, such as Moodle or OLE, this paper proposes to integrate the multimedia-based education to learning systems, and conducts an experiment with the operating system course. We show that exploiting multimedia, such as educational video and smart text, can significantly improve the student's learning performance in terms of exam grade and knowledge transfer. Further, the paper presents a real-world case study depicting how to enhance the performance of learning platform with multimedia semantics.

    DigitalF: Europan Campus Rottal-Inn

    Zeitschriftenartikel

    F. Persia, G. Pilato, Mouzhi Ge, P. Bolzoni, D. D’Auria, S. Helmer

    Improving orienteering-based tourist trip planning with social sensing

    Future Generation Computer Systems, vol. 110, no. 9, pp. 931-945

    2020

    DOI: 10.1016/j.future.2019.10.028

    Abstract anzeigen

    We enhance a tourist trip planning framework based on orienteering with category constraints by adding social sensing. This allows us to customize a user’s experience without putting the burden of preference elicitation on the user. We identify the interests of a user by analyzing their Tweets and then match these interests to descriptions of points of interests. For this analysis we adapt different schemes for social sensing to the needs of our orienteering context and compare them to find the most suitable approach. We show that our technique is fast enough for use in real-time dynamic settings and also has a higher accuracy compared to previous approaches. Additionally, we integrate a more efficient algorithm for solving the orienteering problem, boosting the overall performance and utility of our framework further, as demonstrated by the positive user satisfaction received by real users.

    DigitalF: Europan Campus Rottal-Inn

    Zeitschriftenartikel

    Mouzhi Ge, F. Persia

    A Generalized Evaluation Framework for Multimedia Recommender Systems

    International Journal of Semantic Computing, vol. 12, no. 04, pp. 541-557

    2018

    DOI: 10.1142/S1793351X18500046

    Abstract anzeigen

    With the widespread availability of media technologies, such as real-time streaming, new Internet-of-Thing devices and smart phones, multimedia data are extensively increased and the big multimedia data rapidly spread over various social networks. This has created complexity and information overload for users to choose the suitable multimedia objects. Thus, different multimedia recommender systems have been emerging to help users find the useful multimedia objects that are possibly preferred by the user. However, the evaluation of these multimedia recommender systems is still in an ad-hoc stage. Given the distinct features of multimedia objects, the evaluation criteria adopted from the general recommender systems might not be effectively used to evaluate multimedia recommendations. In this paper, we therefore review and analyze the evaluation criteria that have been used in the previous multimedia recommender system papers. Based on the review, we propose a generalized evaluation framework to guide the researchers and practitioners to perform evaluations, especially user-centric evaluations, for multimedia recommender systems.