Publications

@inproceedings{peretokinOverviewSMARTBEARTechnical2022,

title = {Overview of the SMART-BEAR Technical Infrastructure},

author = { Vadim Peretokin and Ioannis Basdekis and Ioannis Kouris and Jonatan Maggesi and Mario Sicuranza and Qiqi Su and Alberto Acebes and Anca Bucur and Vinod Mukkala and Konstantin Pozdniakov and Christos Kloukinas and Dimitrios Koutsouris and Elefteria Iliadou and Ioannis Leontsinis and Luigi Gallo and Giuseppe De Pietro and George Spanoudakis},

doi = {10.5220/0011082700003188},

isbn = {978-989-758-566-1},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

booktitle = {Proceedings of the 8th International Conference on Information and Communication Technologies for Ageing Well and E-Health},

pages = {117--125},

publisher = {SCITEPRESS - Science and Technology Publications},

address = {Online},

abstract = {This paper describes a cloud-based platform that offers evidence-based, personalised interventions powered by Artificial Intelligence to help support efficient remote monitoring and clinician-driven guidance to people over 65 who suffer or are at risk of hearing loss, cardiovascular diseases, cognitive impairments, balance disorders, and mental health issues. This platform has been developed within the SMART-BEAR integrated project to power its large-scale clinical pilots and comprises a standards-based data harmonisation and management layer, a security component, a Big Data Analytics system, a Clinical Decision Support tool, and a dashboard component for efficient data collection across the pilot sites.},

keywords = {Artificial intelligence, Balance Disorder, Cardiovascular Disease, Cloud computing, Evidence-based, GDPR, Healthcare, Hearing Loss, HL7 FHIR, Interoperability, Semantics},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{lacasciaAutomaticImageAnnotation2018,

title = {Automatic Image Annotation Using Random Projection in a Conceptual Space Induced from Data},

author = { Marco La Cascia and Giorgio Vassallo and Luigi Gallo and Giovanni Pilato and Filippo Vella},

doi = {10.1109/SITIS.2018.00077},

year  = {2018},

date = {2018-11-01},

booktitle = {2018 14th International Conference on Signal-Image Technology Internet-Based Systems (SITIS)},

pages = {464--471},

abstract = {The main drawback of a detailed representation of visual content, whatever is its origin, is that significant features are very high dimensional. To keep the problem tractable while preserving the semantic content, a dimensionality reduction of the data is needed. We propose the Random Projection techniques to reduce the dimensionality. Even though this technique is sub-optimal with respect to Singular Value Decomposition its much lower computational cost make it more suitable for this problem and in particular when computational resources are limited such as in mobile terminals. In this paper we present the use of a ``conceptual'' space, automatically induced from data, to perform automatic image annotation. Images are represented by visual features based on color and texture and arranged as histograms of visual terms and bigrams to partially preserve the spatial information [1]. Using a set of annotated images as training data, the matrix of visual features is built and dimensionality reduction is performed using the Random Projection algorithm. A new unannotated image is then projected into the dimensionally reduced space and the labels of the closest training images are assigned to the unannotated image itself. Experiments on large real collection of images showed that the approach, despite of its low computational cost, is very effective.},

keywords = {Feature extraction, Hidden Markov models, Image annotation, Modeling, Semantics, Visualization},

pubstate = {published},

tppubtype = {inproceedings}

}