Faculty of Electrical Engineering and Information Technologies

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    Deep Learning for Facial Expression and Human Activity Recognition Using Smart Glasses
    (Institute of Electrical and Electronics Engineers (IEEE), 2025-03-14)
    Marinova, Matea
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    Chona, Emilija
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    Kotevski, Andrej
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    Sazdov, Borjan
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    Kiprijanovska, Ivana
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    FedMMA-HAR: Federated Learning for Human Activity Recognition With Missing Modalities Using Head-Worn Wearables
    (Institute of Electrical and Electronics Engineers (IEEE), 2024-10)
    Gobbetti, Alessandro
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    Gjoreski, Martin
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    Gjoreski, Hristijan
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    Lane, Nicholas
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    Langheinrich, Marc
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    Data Science and Machine Learning Teaching Practices with Focus on Vocational Education and Training
    (Vilnius University Press, 2023-04-19)
    Nadzinski, Gorjan
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    Zlatinov, Stefan
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    Dimitrovska, Marija Markovska
    <jats:p>With the development of technology allowing for a rapid expansion of data science and machine learning in our everyday lives, a significant gap is forming in the global job market where the demand for qualified workers in these fields cannot be properly satisfied. This worrying trend calls for an immediate action in education, where these skills must be taught to students at all levels in an efficient and up-to-date manner. This paper gives an overview of the current state of data science and machine learning education globally and both at the high school and university levels, while outlining some illustrative and positive examples. Special focus is given to vocational education and training (VET), where the teaching of these skills is at its very beginning. Also presented and analysed are survey results concerning VET students in Slovenia, Serbia, and North Macedonia, and their knowledge, interests, and prerequisites regarding data science and machine learning. These results confirm the need for development of efficient and accessible curricula and courses on these subjects in vocational schools.</jats:p>
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    Federated Learning for Activity Recognition: A System Level Perspective
    (IEEE, 2023-04)
    Kalabakov, Stefan
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    Jovanovski, Borche
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    Rakovic, Valentin
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    Pfitzner, Bjarne
    The past decade has seen substantial growth in the prevalence and capabilities of wearable devices. For instance, recent human activity recognition (HAR) research has explored using wearable devices in applications such as remote monitoring of patients, detection of gait abnormalities, and cognitive disease identification. However, data collection poses a major challenge in developing HAR systems, especially because of the need to store data at a central location. This raises privacy concerns and makes continuous data collection difficult and expensive due to the high cost of transferring data from a user’s wearable device to a central repository. Considering this, we explore the adoption of federated learning (FL) as a potential solution to address the privacy and cost issues associated with data collection in HAR. More specifically, we investigate the performance and behavioral differences between FL and deep learning (DL) HAR models, under various conditions relevant to real-world deployments. Namely, we explore the differences between the two types of models when (i) using data from different sensor placements, (ii) having access to users with data from heterogeneous sensor placements, (iii) considering bandwidth efficiency, and (iv) dealing with data with incorrect labels. Our results show that FL models suffer from a consistent performance deficit in comparison to their DL counterparts, but achieve these results with much better bandwidth efficiency. Furthermore, we observe that FL models exhibit very similar responses to those of DL models when exposed to data from heterogeneous sensor placements. Finally, we show that the FL models are more robust to data with incorrect labels than their centralized DL counterparts.
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    Item type:Publication,
    11th International Workshop on Human Activity Sensing Corpus and Applications (HASCA)
    (ACM, 2023-10-08)
    Murao, Kazuya
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    Enokibori, Yu
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    Gjoreski, Hristijan
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    Lago, Paula
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    Okita, Tsuyoshi
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    Facial expression recognition using facial mask with EMG sensors
    (2023-05)
    Kiprijanovska, Ivana
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    Sazdov, Borjan
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    Stankoski, Simon
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    Gjoreski, Martin
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    Nduka, Charles
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    Digital Therapeutics Evolution What kind of Research Will Make the Difference in this Area?
    (ACM, 2023-10-08)
    Mayora, Oscar
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    Arnrich, Bert
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    Guerreiro, Tiago
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    Ferreira-Brito, Filipa
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    Luštrek, Mitja
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    Summary of SHL challenge 2023: Recognizing locomotion and transportation mode from GPS and motion sensors
    (2023-09)
    Wang, Lin;
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    Gjoreski, Hristijan
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    Ciliberto, Mathias
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    Lago, Paula
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    Roggen, Daniel
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    Smart Glasses for Gait Analysis of Parkinson’s Disease Patients
    (IEEE, 2023-05-22)
    Kiprijanovska, Ivana
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    Panchevski, Filip
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    Stankoski, Simon
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    Gjoreski, Martin
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    Archer, James
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    Item type:Publication,
    Privacy-aware Human Activity Recognition with Smart Glasses for Digital Therapeutics
    (ACM, 2023-09)
    Sazdov, Borjan
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    Jakimovski Bojan
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    Gjoreski, Martin
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    Nduka, Charles
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    Gjoreski, Hristijan