المؤلفون: Torres-Sospedra, Joaquín, Silva, Ivo, Klus, Lucie, Quezada-Gaibor, Darwin, Crivello, Antonino, Barsocchi, Paolo, Pendão, Cristiano, Lohan, Elena Simona, Nurmi, Jari, Moreira, Adriano

مصطلحات موضوعية: Electrical Engineering and Systems Science - Systems and Control, Computer Science - Machine Learning, Computer Science - Performance

الوصف: The evaluation of Indoor Positioning Systems (IPS) mostly relies on local deployments in the researchers' or partners' facilities. The complexity of preparing comprehensive experiments, collecting data, and considering multiple scenarios usually limits the evaluation area and, therefore, the assessment of the proposed systems. The requirements and features of controlled experiments cannot be generalized since the use of the same sensors or anchors density cannot be guaranteed. The dawn of datasets is pushing IPS evaluation to a similar level as machine-learning models, where new proposals are evaluated over many heterogeneous datasets. This paper proposes a way to evaluate IPSs in multiple scenarios, that is validated with three use cases. The results prove that the proposed aggregation of the evaluation metric values is a useful tool for high-level comparison of IPSs.
Comment: to appear in 2021 International Conference on Indoor Positioning and Indoor Navigation (IPIN), 29 Nov. - 2 Dec. 2021, Lloret de Mar, Spain

الوصول الحر: http://arxiv.org/abs/2109.09436Test

المؤلفون: Panigrahi, Susant Kumar, Tripathy, Santosh Kumar, Bhowmick, Anirban, Satapathy, Santosh Kumar, Barsocchi, Paolo, Bhoi, Akash Kumar

المساهمون: CHIPS project “Cyber and Human Intelligence for Physical Systems”

المصدر: IEEE Access ; volume 12, page 25090-25105 ; ISSN 2169-3536

الإتاحة: https://doi.org/10.1109/access.2024.3364397Test
http://xplorestaging.ieee.org/ielx7/6287639/10380310/10430182.pdf?arnumber=10430182Test

المؤلفون: Girolami, Michele, Mavilia, Fabio, Furfari, Francesco, Barsocchi, Paolo

المساهمون: European Union - Next Generation EU

المصدر: IEEE Journal of Indoor and Seamless Positioning and Navigation ; volume 2, page 36-50 ; ISSN 2832-7322

الإتاحة: https://doi.org/10.1109/jispin.2023.3345268Test
http://xplorestaging.ieee.org/ielx7/9955032/10378862/10366502.pdf?arnumber=10366502Test

المؤلفون: Furfari, Francesco, Girolami, Michele, Barsocchi, Paolo

المصدر: IEEE Journal of Indoor and Seamless Positioning and Navigation ; volume 2, page 130-142 ; ISSN 2832-7322

الإتاحة: https://doi.org/10.1109/jispin.2024.3377146Test
http://xplorestaging.ieee.org/ielx7/9955032/10378862/10471883.pdf?arnumber=10471883Test

المؤلفون: Girolami, Michele, La Rosa, Davide, Barsocchi, Paolo

المساهمون: European Union, Office of Population Affairs, Regione Toscana

المصدر: Data in Brief ; volume 53, page 110215 ; ISSN 2352-3409

مصطلحات موضوعية: Multidisciplinary

الإتاحة: https://doi.org/10.1016/j.dib.2024.110215Test
https://api.elsevier.com/content/article/PII:S2352340924001860?httpAccept=text/xmlTest
https://api.elsevier.com/content/article/PII:S2352340924001860?httpAccept=text/plainTest

المؤلفون: Girolami, Michele, La Rosa, Davide, Barsocchi, Paolo

المساهمون: Office of Population Affairs, European Commission, Regione Toscana

المصدر: Ad Hoc Networks ; volume 154, page 103367 ; ISSN 1570-8705

الإتاحة: https://doi.org/10.1016/j.adhoc.2023.103367Test
https://api.elsevier.com/content/article/PII:S1570870523002871?httpAccept=text/xmlTest
https://api.elsevier.com/content/article/PII:S1570870523002871?httpAccept=text/plainTest

المؤلفون: Belli, Dimitri, Barsocchi, Paolo, Palumbo, Filippo

المساهمون: European Commission

المصدر: Internet of Things ; volume 25, page 101005 ; ISSN 2542-6605

مصطلحات موضوعية: Management of Technology and Innovation, Artificial Intelligence, Computer Science Applications, Hardware and Architecture, Engineering (miscellaneous), Information Systems, Computer Science (miscellaneous), Software

الإتاحة: https://doi.org/10.1016/j.iot.2023.101005Test
https://api.elsevier.com/content/article/PII:S2542660523003281?httpAccept=text/xmlTest
https://api.elsevier.com/content/article/PII:S2542660523003281?httpAccept=text/plainTest

المؤلفون: Torres-Sospedra, Joaquín, Pendão, Cristiano, Silva, Ivo, Meneses, Filipe, Quezada-Gaibor, Darwin, Montoliu, Raúl, Crivello, Antonino, Barsocchi, Paolo, Pérez-Navarro, Antoni, Moreira, Adriano

مصطلحات موضوعية: Wi-Fi Fingerprinting, Received Signal Strength, k–Nearest Neighbor, Reproducibility, Replicability, Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática, Cidades e comunidades sustentáveis

الوصف: Microsoft proposed RADAR in 2000, the first indoor positioning system based on Wi-Fi fingerprinting. Since then, the indoor research community has worked not only to improve the base estimator but also on finding an optimal RSS data representation. The long-term objective is to find a positioning system that minimises the mean positioning error. Despite the relevant advances in the last 23 years, a disruptive solution has not been reached yet. The evaluation with non-open datasets and comparisons with non-optimized baselines make the analysis of the current status of fingerprinting for indoor positioning difficult. In addition, the lack of implementation details or data used for evaluation in several works make results reproducibility impossible. This paper focuses on providing a comprehensive analysis of fingerprinting with k-NN and settling the basement for replicability and reproducibility in further works, targeting to bring relevant information about k-NN when it is used as a baseline comparison of advanced fingerprint-based methods. ; The authors gratefully acknowledge funding from projects ORIENTATE H2020-MSCA-IF GA.101023072; FCT UIDB/00319/2020; CYTED Network “GeoLibero”; PID2021-122642OB-C42; and PID2021-122642OB-C44.

وصف الملف: application/pdf

العلاقة: info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00319%2F2020/PT; info:eu-repo/grantAgreement/EC/H2020/101023072/EU; https://ieeexplore.ieee.org/document/10332535Test; Torres-Sospedra, J., Pendão, C., Silva, I., Meneses, F., Quezada-Gaibor, D., Montoliu, R., … Moreira, A. (2023, September 25). Let’s Talk about k-NN for Indoor Positioning: Myths and Facts in RF-based Fingerprinting. 2023 13th International Conference on Indoor Positioning and Indoor Navigation (IPIN). IEEE. http://doi.org/10.1109/ipin57070.2023.10332535Test; https://hdl.handle.net/1822/90430Test; 979-8-3503-2011-4

الإتاحة: https://doi.org/10.1109/IPIN57070.2023.10332535Test
https://doi.org/10.1109/ipin57070.2023.10332535Test
https://hdl.handle.net/1822/90430Test

المؤلفون: Carboni, Andrea, Russo, Dario, Moroni, Davide, Barsocchi, Paolo

المساهمون: CNR Istituto di Scienza e Tecnologie dell’Informazione “A. Faedo” Pisa (CNR, National Research Council of Italy

المصدر: EISSN: 2673-253X ; Frontiers in Digital Health ; https://hal.science/hal-03987992Test ; Frontiers in Digital Health, 2023, 4, ⟨10.3389/fdgth.2022.934609⟩

مصطلحات موضوعية: AHA AAL privacy by design ISA IS, AHA, AAL, privacy by design, ISA, IS, [INFO]Computer Science [cs], [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology

الوصف: Privacy by design within a system for assisted living, personalised care, and wellbeing is crucial to protect users from misuse of the data collected about their health. Especially if the information is collected through audio-video devices, the question is even more delicate due to the nature of these data. In addition to guaranteeing a high level of privacy, it is necessary to reassure end users about the correct use of these streams. The evolution of data analysis techniques began to take on an important role and increasingly defined characteristics in recent years. The purpose of this paper is twofold: on the one hand, it presents a state of the art about privacy in European Active Healthy Ageing/Active Healthy Ageing projects, with a focus on those related to audio and video processing. On the other hand, it proposes a methodology, developed in the context of the European project PlatfromUptake.eu, to identify clusters of stakeholders and application dimensions (technical, contextual, and business), define their characteristics, and show how privacy constraints affect them. From this study, we then generated a Strengths, Weaknesses, Opportunities, and Threats analysis in which we aim to identify the critical features connected to the selection and involvement of relevant stakeholders for the success of a project. Applying this type of methodology to the initial stages of a project allows understanding of which privacy issues could be related to the various stakeholder groups and which problems can then affect the correct development of the project. The idea is, therefore, to suggest a privacy-by-design approach according to the categories of stakeholders and project dimensions. The analysis will cover technical aspects, legislative and policies-related aspects also regarding the point of view of the municipalities, and aspects related to the acceptance and, therefore, to the perception of the safety of these technologies by the final end users.

العلاقة: hal-03987992; https://hal.science/hal-03987992Test; https://hal.science/hal-03987992/documentTest; https://hal.science/hal-03987992/file/fdgth-04-934609.pdfTest

الإتاحة: https://doi.org/10.3389/fdgth.2022.934609Test
https://hal.science/hal-03987992Test
https://hal.science/hal-03987992/documentTest
https://hal.science/hal-03987992/file/fdgth-04-934609.pdfTest

المؤلفون: Sirisha, U., Praveen, S. Phani, Srinivasu, Parvathaneni Naga, Barsocchi, Paolo, Bhoi, Akash Kumar

المصدر: International Journal of Computational Intelligence Systems ; volume 16, issue 1 ; ISSN 1875-6883

مصطلحات موضوعية: Computational Mathematics, General Computer Science

الوصف: Object detection is a critical and complex problem in computer vision, and deep neural networks have significantly enhanced their performance in the last decade. There are two primary types of object detectors: two stage and one stage. Two-stage detectors use a complex architecture to select regions for detection, while one-stage detectors can detect all potential regions in a single shot. When evaluating the effectiveness of an object detector, both detection accuracy and inference speed are essential considerations. Two-stage detectors usually outperform one-stage detectors in terms of detection accuracy. However, YOLO and its predecessor architectures have substantially improved detection accuracy. In some scenarios, the speed at which YOLO detectors produce inferences is more critical than detection accuracy. This study explores the performance metrics, regression formulations, and single-stage object detectors for YOLO detectors. Additionally, it briefly discusses various YOLO variations, including their design, performance, and use cases.

الإتاحة: https://doi.org/10.1007/s44196-023-00302-wTest