Browsing Data Science Institute by Author "d’Aquin, Mathieu"

Now showing items 1-11 of 11

    • AFEL-Analytics for Everyday Learning 

      d’Aquin, Mathieu; Kowald, Dominik; Fessl, Angela; Lex, Elisabeth; Thalmann, Stefan (ACM, 2018-04-23)
      The goal of AFEL is to develop, pilot and evaluate methods and applications, which advance informal/collective learning as it surfaces implicitly in online social environments. The project is following a multi-disciplinary, ...

    • AFEL: Towards measuring online activities contributions to self-directed learning 

      d’Aquin, Mathieu; Adamou, Alessandro; Dietze, Stefan; Fetahu, Besnik; Gadiraju, Ujwal; Hasani-Mavriqi, Ilire; Holtz, Peter; Kimmerle, Joachim; Kowald, Dominik; Lex, Elisabeth; López Sola, Susana; Maturana, Ricardo A.; Sabol, Vedran; Troullinou, Pinelopi; Veas, Eduardo (CEUR-WS.org, 2017-09-12)
      More and more learning activities take place online in a selfdirected manner. Therefore, just as the idea of self-tracking activities for fitness purposes has gained momentum in the past few years, tools and methods for ...

    • Analysing and improving embedded markup of learning resources on the web 

      Dietze, Stefan; Taibi, Davide; Yu, Ran; Barker, Phil; d’Aquin, Mathieu (ACM, 2017-04-03)
      Web-scale reuse and interoperability of learning resources have been major concerns for the technology-enhanced learning community. While work in this area traditionally focused on learning resource metadata, provided ...

    • Facilitating scientometrics in learning analytics and educational data mining - The LAK dataset 

      Dietze, Stefan; Taibi, Davide; d’Aquin, Mathieu (IOS Press, 2016-11-06)
      The Learning Analytics and Knowledge (LAK) Dataset represents an unprecedented corpus which exposes a near complete collection of bibliographic resources for a specific research discipline, namely the connected areas of ...

    • Measuring accuracy of triples in knowledge graphs 

      Liu, Shuangyan; d’Aquin, Mathieu; Motta, Enrico (Springer, 2017-06-19)
      An increasing amount of large-scale knowledge graphs have been constructed in recent years. Those graphs are often created from text-based extraction, which could be very noisy. So far, cleaning knowledge graphs are ...

    • An ontology-based approach to improve the accessibility of ROS-based robotic systems 

      Tiddi, Ilaria; Bastianelli, Emanuele; Bardaro, Gianluca; d’Aquin, Mathieu; Motta, Enrico (ACM, 2017-12-04)
      The focus of this work is to exploit ontologies to make robotic systems more accessible to non-expert users, therefore supporting the deployment of robot-integrated applications. Due to the increasing number of robotic ...

    • Privacy, security and policies: A review of problems and solutions with semantic web technologies 

      Kirrane, Sabrina; Villata, Serena; d’Aquin, Mathieu (IOS Press, 2018)
      Semantic Web technologies aim to simplify the distribution, sharing and exploitation of information and knowledge, across multiple distributed actors on the Web. As with all technologies that manipulate information, there ...

    • Propagating data policies: a user study 

      Daga, Enrico; d’Aquin, Mathieu; Motta, Enrico (ACM, 2017-11-04)
      When publishing data, data licences are used to specify the actions that are permitted or prohibited, and the duties that target data consumers must comply with. However, in complex environments such as a smart city ...

    • Re-coding Black Mirror Chairs' Welcome & Organization 

      Troullinou, Pinelopi; d’Aquin, Mathieu; Tiddi, Ilaria (ACM, 2018-04-23)
      This volume of proceedings presents the papers from the 2nd edition of the interdisciplinary workshop Re-coding Black Mirror, held on April 24, 2018 in Lyon, France and co-located with The WEB Conference (WWW2018). ...

    • Synergy between embedding and protein functional association networks for drug label prediction using harmonic function 

      Timilsina, Mohan; Mc Kernan, Declan Patrick; Yang, Haixuan; d’Aquin, Mathieu (ACM and IEEE, 2020-10-16)
      Semi-Supervised Learning (SSL) is an approach to machine learning that makes use of unlabeled data for training with a small amount of labeled data. In the context of molecular biology and pharmacology, one can take advantage ...

    • Unsupervised learning for understanding student achievement in a distance learning setting 

      Liu, Shuangyan; d’Aquin, Mathieu (IEEE, 2017-04-25)
      Many factors could affect the achievement of students in distance learning settings. Internal factors such as age, gender, previous education level and engagement in online learning activities can play an important role ...