Typology
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2017
Report
Unknown
SoBigData - Periodic dissemination and impact report and plan for following year 1
Grossi V., Rapisarda B., Falchi C.
This first periodic report includes the description of dissemination and impact activities undertaken and scientific papers published, as well as the planning for the second period. Furthermore, It describes the efforts to be made to reach as wide an audience as possible, and the multiple strategies employed by the consortium. This deliverable covers a time period starting from December 2015 (M4) to February 2017 (M18).Source: Project report, SoBigData, Deliverable D3.4, 2017
Project(s): SoBigData
Grossi V., Rapisarda B., Falchi C.
This first periodic report includes the description of dissemination and impact activities undertaken and scientific papers published, as well as the planning for the second period. Furthermore, It describes the efforts to be made to reach as wide an audience as possible, and the multiple strategies employed by the consortium. This deliverable covers a time period starting from December 2015 (M4) to February 2017 (M18).Source: Project report, SoBigData, Deliverable D3.4, 2017
Project(s): SoBigData
See at: CNR ExploRA
2022
Report
Open Access
Processo di gestione dei flussi amministrativi delle certificazioni degli apparati per la memorizzazione elettronica e la trasmissione telematica dei dati dei corrispettivi giornalieri
Spagnolo G. O., Cempini A., Falchi C., Lami G., Pierotti A., Puntoni M.
Questo Rapporto Tecnico intende fornire una chiara e completa descrizione del nuovo processo di gestione dei flussi amministrativi nel contesto delle Certificazioni degli apparati per la memorizzazione elettronica e la trasmissione telematica dei dati dei corrispettivi giornalieri di cui all'art. 2, comma 1, del decreto legislativo 5 agosto 2015, n. 127 ai sensi del D.M. 23 marzo 1983 e secondo il provvedimento dell'Agenzia delle Entrate del 0182017 del 28 ottobre 2016 e successive modificazioni ed integrazioni.Source: ISTI Technical Report, ISTI-2022-TR/020, pp.1–9, 2022
DOI: 10.32079/isti-tr-2022/020
Metrics:
Spagnolo G. O., Cempini A., Falchi C., Lami G., Pierotti A., Puntoni M.
Questo Rapporto Tecnico intende fornire una chiara e completa descrizione del nuovo processo di gestione dei flussi amministrativi nel contesto delle Certificazioni degli apparati per la memorizzazione elettronica e la trasmissione telematica dei dati dei corrispettivi giornalieri di cui all'art. 2, comma 1, del decreto legislativo 5 agosto 2015, n. 127 ai sensi del D.M. 23 marzo 1983 e secondo il provvedimento dell'Agenzia delle Entrate del 0182017 del 28 ottobre 2016 e successive modificazioni ed integrazioni.Source: ISTI Technical Report, ISTI-2022-TR/020, pp.1–9, 2022
DOI: 10.32079/isti-tr-2022/020
Metrics:
See at:
ISTI Repository 
| CNR ExploRA
2022
Dataset
Open Access
MOBDrone: a large-scale drone-view dataset for man overboard detection
Cafarelli D., Ciampi L., Vadicamo L., Gennaro C., Berton A., Paterni M., Benvenuti C., Passera M., Falchi F.
The Man OverBoard Drone (MOBDrone) dataset is a large-scale collection of aerial footage images. It contains 126,170 frames extracted from 66 video clips gathered from one UAV flying at an altitude of 10 to 60 meters above the mean sea level. Images are manually annotated with more than 180K bounding boxes localizing objects belonging to 5 categories --- person, boat, lifebuoy, surfboard, wood. More than 113K of these bounding boxes belong to the person category and localize people in the water simulating the need to be rescued.
Cafarelli D., Ciampi L., Vadicamo L., Gennaro C., Berton A., Paterni M., Benvenuti C., Passera M., Falchi F.
The Man OverBoard Drone (MOBDrone) dataset is a large-scale collection of aerial footage images. It contains 126,170 frames extracted from 66 video clips gathered from one UAV flying at an altitude of 10 to 60 meters above the mean sea level. Images are manually annotated with more than 180K bounding boxes localizing objects belonging to 5 categories --- person, boat, lifebuoy, surfboard, wood. More than 113K of these bounding boxes belong to the person category and localize people in the water simulating the need to be rescued.
See at:
ISTI Repository | CNR ExploRA | zenodo.org
2017
Report
Unknown
SoBigData - VA e-Infrastructure service provision and operation report 1
Trasarti R., Pagano P., Falchi C., Grossi V., Rapisarda B.
The deliverable present the status of the SoBigData platform as an evolving e-infrastructure where the partners are continuosly adding new contents and improving the presentation of them. The virtual research enviroments (VREs) already integrated will be described and monitored with a set of KPIs describing the number of access, the experiments done and the social network activities related to them. Moreover a description of the VREs which are not yet public but are under an internal review phase will be described in order to understand how the consortium is prooceding in integrating resources to the e-infrastructure. An important note is the fact that this deliverable does not contain the assessment from the Advisory Board as described in the DOW, this due the fact that the board is not yet formed. Anyway the SoBigData Platform is begin used by the partner for inserting resources only in the last 6 months and therefore it is in stage where the content vary greatly (in the last month the resources in the catalogue doubled).Source: Project report, SoBigData, Deliverable D7.1, 2017
Project(s): SoBigData
Trasarti R., Pagano P., Falchi C., Grossi V., Rapisarda B.
The deliverable present the status of the SoBigData platform as an evolving e-infrastructure where the partners are continuosly adding new contents and improving the presentation of them. The virtual research enviroments (VREs) already integrated will be described and monitored with a set of KPIs describing the number of access, the experiments done and the social network activities related to them. Moreover a description of the VREs which are not yet public but are under an internal review phase will be described in order to understand how the consortium is prooceding in integrating resources to the e-infrastructure. An important note is the fact that this deliverable does not contain the assessment from the Advisory Board as described in the DOW, this due the fact that the board is not yet formed. Anyway the SoBigData Platform is begin used by the partner for inserting resources only in the last 6 months and therefore it is in stage where the content vary greatly (in the last month the resources in the catalogue doubled).Source: Project report, SoBigData, Deliverable D7.1, 2017
Project(s): SoBigData
See at: CNR ExploRA
2022
Conference article
Open Access
MOBDrone: a drone video dataset for Man OverBoard Rescue
Cafarelli D., Ciampi L., Vadicamo L., Gennaro C., Berton A., Paterni M., Benvenuti C., Passera M., Falchi F.
Modern Unmanned Aerial Vehicles (UAV) equipped with cameras can play an essential role in speeding up the identification and rescue of people who have fallen overboard, i.e., man overboard (MOB). To this end, Artificial Intelligence techniques can be leveraged for the automatic understanding of visual data acquired from drones. However, detecting people at sea in aerial imagery is challenging primarily due to the lack of specialized annotated datasets for training and testing detectors for this task. To fill this gap, we introduce and publicly release the MOBDrone benchmark, a collection of more than 125K drone-view images in a marine environment under several conditions, such as different altitudes, camera shooting angles, and illumination. We manually annotated more than 180K objects, of which about 113K man overboard, precisely localizing them with bounding boxes. Moreover, we conduct a thorough performance analysis of several state-of-the-art object detectors on the MOBDrone data, serving as baselines for further research.Source: ICIAP 2022 - 21st International Conference on Image Analysis and Processing, pp. 633–644, Lecce, Italia, 23-27/05/2022
DOI: 10.1007/978-3-031-06430-2_53
Metrics:
Cafarelli D., Ciampi L., Vadicamo L., Gennaro C., Berton A., Paterni M., Benvenuti C., Passera M., Falchi F.
Modern Unmanned Aerial Vehicles (UAV) equipped with cameras can play an essential role in speeding up the identification and rescue of people who have fallen overboard, i.e., man overboard (MOB). To this end, Artificial Intelligence techniques can be leveraged for the automatic understanding of visual data acquired from drones. However, detecting people at sea in aerial imagery is challenging primarily due to the lack of specialized annotated datasets for training and testing detectors for this task. To fill this gap, we introduce and publicly release the MOBDrone benchmark, a collection of more than 125K drone-view images in a marine environment under several conditions, such as different altitudes, camera shooting angles, and illumination. We manually annotated more than 180K objects, of which about 113K man overboard, precisely localizing them with bounding boxes. Moreover, we conduct a thorough performance analysis of several state-of-the-art object detectors on the MOBDrone data, serving as baselines for further research.Source: ICIAP 2022 - 21st International Conference on Image Analysis and Processing, pp. 633–644, Lecce, Italia, 23-27/05/2022
DOI: 10.1007/978-3-031-06430-2_53
Metrics:
See at:
ISTI Repository | link.springer.com 
| CNR ExploRA