2021
Autores
Sato, M; Jatowt, A; Duan, YJ; Campos, R; Yoshikawa, M;
Publicação
2021 ACM/IEEE JOINT CONFERENCE ON DIGITAL LIBRARIES (JCDL 2021)
Abstract
Our society generates massive amounts of digital data, significant portion of which is being archived and made accessible to the public for the current and future use. In addition, historical born-analog documents are being increasingly digitized and included in document archives which are available online. Professionals who use document archives tend to know what they wish to search for. Yet, if the results are to be useful and attractive for ordinary users they need to contain content which is interesting and familiar. However, the state-of-the-art retrieval methods for document archives basically apply same techniques as search engines for synchronic document collections. In this paper, we introduce a novel concept of estimating the relation of archival documents to the present times, called contemporary relevance. Contemporary relevance can be used for improving access to archival document collections so that users have higher probability of finding interesting or useful content. We then propose an effective method for computing contemporary relevance degrees of news articles using Learning to Rank with a range of diverse features, and we successfully test it on the New York Times Annotated document collection. Our proposal offers a novel paradigm of information access to archival document collections by incorporating the context of contemporary time.
2021
Autores
Karácsony, T; Loesch Biffar, AM; Vollmar, C; Noachtar, S; Cunha, JPS;
Publicação
BHI 2021 - 2021 IEEE EMBS International Conference on Biomedical and Health Informatics, Proceedings
Abstract
Epilepsy is a major neurological disorder affecting approximately 1% of the world population, where seizure semiology is an essential tool for clinical evaluation of seizures. This includes qualitative visual inspection of videos from the seizures in epilepsy monitoring units by epileptologists. In order to support this clinical diagnosis process, promising deep learning-based systems were proposed. However, these indicate that video datasets of epileptic seizures are still rare and limited in size. In order to enable the full potential of AI systems for epileptic seizure diagnosis support and research, a novel collaborative development framework is proposed for a scalable DL-assisted clinical research and diagnosis support of epileptic seizures. The designed cloud-based approach integrates our deployed and tested NeuroKinect data acquisition pipeline into an MLOps framework to scale data set extension and analysis to a multi-clinical utilization. The proposed development framework incorporates an MLOps approach, to ensure convenient collaboration between clinicians and data scientists, providing continuous advantages to both user groups. It addresses methods for efficient utilization of HW, SW and human resources. In the future, the system is going to be expanded with several AI-based tools. Such as DL-based automated 3D motion capture (MoCap), 3D movement analysis support, quantitative seizure semiology analysis tools, video-based MOI and seizure classification. © 2021 IEEE
2021
Autores
Pacheco, H; Macedo, N;
Publicação
International Journal of Robotic Computing
Abstract
2020
Autores
Krassmann, AL; Rocha Mazzuco, AEd; Melo, M; Bessa, M; Bercht, M;
Publicação
Proceedings of the 12th International Conference on Computer Supported Education, CSEDU 2020, Prague, Czech Republic, May 2-4, 2020, Volume 1.
Abstract
This case study presents a virtual reality experts' evaluation of a desktop-based virtual world developed towards distance education, under the perspectives of usability and sense of presence, which are considered factors that can potentially influence learning outcomes. Among the results, data from usability and sense of presence were positively correlated. The sense of presence was achieved, with participants losing track of time while performing the activity. Experts agreed that the virtual world is easy to use and can prepare students for the real-world task. The findings outline positive and negative points that must be addressed in order to optimize the experience of distance education students. Copyright
2020
Autores
Morais, C; Pedrosa, D; Rocio, V; Cravino, J; Morgado, L;
Publicação
Technology and Innovation in Learning, Teaching and Education - Second International Conference, TECH-EDU 2020, Vila Real, Portugal, December 2-4, 2020, Proceedings, 3
Abstract
We used BPMN diagrams to identify indicators that can assist teachers in their intervention actions to support students' self-regulation and co-regulation in an asynchronous e-learning context. The use of BPMN modeling, by making explicit the tasks and procedures implicit in the intervention of the e-learning teacher, also exposed which data were available for developing decision-support indicators, as well as the relevant moments for carrying out interventions. Such indicators can help e-learning teachers focus their interventions to support self-regulation and co-regulation of learning, as well as enabling the creation of live data dashboards to support decision-making for those interventions, thus this process can contribute to devise better instruments for teacher intervention in support of self-regulation and co-regulation of student learning. © 2021, Springer Nature Switzerland AG.
2020
Autores
Coelho, H; Melo, M; Martins, J; Bessa, M;
Publicação
MULTIMEDIA TOOLS AND APPLICATIONS
Abstract
In the original publication, Figs. 1 and 2 were interchange and the citation of Fig. 1 in the third paragraph of section 2.2 Authoring tools for multisensory VR experiences should be removed.
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