Paulo Ferreira

• Tavakolisomeh, Sanaz; Bruno, Rodrigo & Ferreira, Paulo (2023). BestGC: An Automatic GC Selector. IEEE Access. ISSN 2169-3536. 11, p. 72357–72373. doi: 10.1109/ACCESS.2023.3294398. Full text in Research Archive
• Naghipour Vijouyeh, Lyla; Bruno, Rodrigo & Ferreira, Paulo (2023). EdgeEmu - Emulator for Android Edge Devices. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 13909, p. 110–127. doi: 10.1007/978-3-031-35260-7_7.
• Helmy, Maged; Truong, Trung Tuyen; Jul, Eric Bartley & Ferreira, Paulo (2022). Deep Learning and Computer Vision Techniques for Microcirculation Analysis: A Review. Patterns. ISSN 2666-3899. 4(1). doi: 10.1016/j.patter.2022.100641. Full text in Research Archive
• Helmy, Maged; Truong, Trung Tuyen; Dykyy, Anastasiya; Ferreira, Paulo & Jul, Eric Bartley (2022). CapillaryNet: An automated system to quantify skin capillary density and red blood cell velocity from handheld vital microscopy. Artificial Intelligence in Medicine. ISSN 0933-3657. 127. doi: 10.1016/j.artmed.2022.102287. Full text in Research Archive
• Kamalian, Mahdieh; Ferreira, Paulo & Jul, Eric Bartley (2022). A survey on local transport mode detection on the edge of the network. Applied intelligence (Boston). ISSN 0924-669X. 52(14), p. 16021–16050. doi: 10.1007/s10489-022-03214-y. Full text in Research Archive Show summary

  We present a survey of smartphone-based Transport Mode Detection (TMD). We categorize TMD solutions into local and remote; the first ones are addressed in this article. A local approach performs the following steps in the smartphone (and not in some faraway cloud servers): 1) data collection or sensing, 2) preprocessing, 3) feature extraction, and 4) classification (with a previous training phase). A local TMD approach outperforms a remote approach due to less delay, improved privacy, no need for Internet connection, better or equal accuracy and smaller data size. Therefore, we present local TMD solutions taking into account the above mentioned four steps and analyze them according to the most relevant requirements: accuracy, delay, resources consumption and generalization. To achieve the highest accuracy (100%), studies used a different combination of sensors, features and Machine Learning (ML) algorithms. The results suggest that accelerometer and GPS (Global Position System) are the most useful sensors for data collection. Discriminative ML algorithms, such as random forest, outperform the other algorithms for classification. Some solutions improved the delay of the proposed system by using a small window size and a local approach. A few studies could improve battery usage of their system by utilizing low battery-consuming sensors (e.g., accelerometer) and low sampling rate (e.g., 10Hz). CPU usage is primarily dependent on data collection, while memory usage is related to the features and complexity of the ML algorithm. Finally, the generalization requirement is met in studies that consider user, location and position independency into account.

• Tavakolisomeh, Sanaz; Bruno, Rodrigo & Ferreira, Paulo (2021). Selecting a GC for Java Applications. Norsk Informatikkonferanse (NIK). Full text in Research Archive
• Matre, Dagfinn; Christensen, Jan Olav; Mork, Paul Jarle; Ferreira, Paulo; Sand, Trond & Nilsen, Kristian Bernhard (2021). Shift work, inflammation and musculoskeletal pain. The HUNT Study. Occupational Medicine. ISSN 0962-7480. 71(9), p. 422–427. doi: 10.1093/occmed/kqab133. Show summary

  Background: Studies have indicated that shift work, in particular night work, is associated with chronic musculoskeletal pain but the mechanisms are unclear. It has been suggested that sleep disturbance, a common complaint among shift and night workers, may induce low-grade inflammation as well as heightened pain sensitivity. Aims: Firstly, this study was aimed to examine the cross-sectional associations between shift work, C-reactive protein (CRP) level and chronic musculoskeletal pain, and secondly, to analyse CRP as a mediator between shift work and chronic musculoskeletal pain. Methods: The study included 23 223 vocationally active women and men who participated in the HUNT4 Survey of the Trøndelag Health Study (HUNT). Information was collected by questionnaires, interviews, biological samples and clinical examination. Results: Regression analyses adjusted for sex, age and education revealed significant associations between shift work and odds of any chronic musculoskeletal pain (odd ratio [OR] 1.11, 95% confidence interval [CI] 1.04-1.19), between shift work and CRP level (OR 1.09, 95% CI 1.03-1.16) and between CRP level 3.00-10 mg/L and any chronic musculoskeletal pain (OR 1.38, 95% CI 1.27-1.51). Shift work and CRP were also associated with number of chronic pain sites. Mediation analysis indicated that shift work was indirectly associated with any chronic musculoskeletal pain through CRP (OR 1.03, 95% CI 1.01-1.06). Conclusions: The results support the hypothesis that shift work is associated with chronic musculoskeletal pain, and that systemic inflammation may be a biological mechanism linking shift work to chronic pain.

• Ferreira, Paulo; Zavgorodnii, Constantin & Veiga, Luìs (2020). edgeTrans - Edge transport mode detection. Pervasive and Mobile Computing. ISSN 1574-1192. 69. doi: 10.1016/j.pmcj.2020.101268. Full text in Research Archive
• Bruno, Rodrigo; Patricio, Duarte; Simão, José; Veiga, Luìs & Ferreira, Paulo (2019). Runtime Object Lifetime Profiler for Latency Sensitive Big Data Applications, EuroSys '19: Fourteenth EuroSys Conference 2019. Association for Computing Machinery (ACM). ISSN 978-1-4503-6281-8. doi: 10.1145/3302424.3303988.
• Ferreira, Paulo; Zabolotny, Andriy & Barreto, João (2019). Bicycle Mode Activity Detection with Bluetooth Low Energy Beacons. In Gkoulalas-Divanis, Aris; Marchetti, Mirco & Avresky, Dimiter R. (Ed.), 2019 IEEE 18th International Symposium on Network Computing and Applications (NCA). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-7281-2522-0. doi: 10.1145/3412841.3441965.
• Tavakolisomeh, Sanaz; Shimchenko, Marina; Österlund, Erik; Bruno, Rodrigo; Ferreira, Paulo & Wrigstad, Tobias (2008). Heap Size Adjustment with CPU Control. In Vasconcelos, Vasco Thudichum (Eds.), SPLASH '23: 2023 ACM SIGPLAN International Conference on Systems, Programming, Languages, and Applications: Software for Humanity. Association for Computing Machinery (ACM). ISSN 979-8-4007-0384-3. p. 2023–2023. doi: 10.1145/3617651.3622988. Full text in Research Archive

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• Ferreira, Paulo (2018). Proceedings of the 19th International Middleware Conference. Association for Computing Machinery (ACM). ISBN 978-1-4503-5702-9. 299 p.

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• Ferreira, Paulo (2023). Vil bruke KI til å analysere transportdata fra smarttelefoner – kan føre til nye billettsystemer. [Newspaper]. Forskningparken.
• Kamalian, Mahdieh & Ferreira, Paulo (2022). FogTMDetector - Fog Based Transport Mode Detection using Smartphones. Show summary

  A user’s transport mode (e.g., walk, car, etc.) can be detected by using a smartphone. Such devices exist in a great number with enough computation power and sensors to run a classifier (i.e., for transport mode detection). Using a smartphone in a fog environment ensures low latency, high generalization, high accuracy, and low battery consumption. We propose a fog- based real-time (at human time scale) transport mode detection, called FogTMDetector; it consists of a Random Forest classifier trained with magnetometer, accelerometer, and GPS data. The overall accuracy achieved by our system is 93% when detecting 8 different modes (i.e., stationary, walk, bicycle, car, bus, train, tram, and subway). We compared FogTMDetector with another recent system (called EdgeTrans). The comparison results suggest that our solution achieves 10% higher motorized accuracy (i.e., 94.4%) with more fine-grained motorized transport modes (i.e., subway, tram, etc.) thanks to the magnetometer sensor readings. FogTMDetector uses a low sampling rate (1Hz) for logging accelerometer and magnetometer and (every 10 seconds) for GPS to ensure low battery consumption. FogTMDetector is also generalizable as it is robust against variation of users and smartphone positions.

• Ferreira, Paulo (2018). Foreword for the 19th ACM/IFIP/USENIX Middleware Conference. In Ferreira, Paulo (Eds.), Proceedings of the 19th International Middleware Conference. Association for Computing Machinery (ACM). ISSN 978-1-4503-5702-9. p. 1–3.
• Ferreira, Paulo & Bruno, Rodrigo (2018). Live Migration and Garbage Collection for Big Data Java Applications.

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