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description Publicationkeyboard_double_arrow_right Article , Conference object 2021 ItalyPublisher:MDPI AG Authors: Chiara Giola; Piero Danti; Sandro Magnani;Chiara Giola; Piero Danti; Sandro Magnani;In the age of AI, companies strive to extract benefits from data. In the first steps of data analysis, an arduous dilemma scientists have to cope with is the definition of the ’right’ quantity of data needed for a certain task. In particular, when dealing with energy management, one of the most thriving application of AI is the consumption’s optimization of energy plant generators. When designing a strategy to improve the generators’ schedule, a piece of essential information is the future energy load requested by the plant. This topic, in the literature it is referred to as load forecasting, has lately gained great popularity; in this paper authors underline the problem of estimating the correct size of data to train prediction algorithms and propose a suitable methodology. The main characters of this methodology are the Learning Curves, a powerful tool to track algorithms performance whilst data training-set size varies. At first, a brief review of the state of the art and a shallow analysis of eligible machine learning techniques are offered. Furthermore, the hypothesis and constraints of the work are explained, presenting the dataset and the goal of the analysis. Finally, the methodology is elucidated and the results are discussed.
Flore (Florence Rese... arrow_drop_down Flore (Florence Research Repository)Article . 2021Data sources: Flore (Florence Research Repository)https://doi.org/10.3390/engpro...Conference object . 2021 . Peer-reviewedLicense: CC BYData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/engproc2021005038&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routeshybrid 1 citations 1 popularity Average influence Average impulse Average Powered by BIP!more_vert Flore (Florence Rese... arrow_drop_down Flore (Florence Research Repository)Article . 2021Data sources: Flore (Florence Research Repository)https://doi.org/10.3390/engpro...Conference object . 2021 . Peer-reviewedLicense: CC BYData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/engproc2021005038&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Conference object 2021 ItalyPublisher:ACM Authors: Gharib M.; Zoppi T.; Bondavalli A.;Gharib M.; Zoppi T.; Bondavalli A.;handle: 2158/1238469
Nowadays, Machine Learning (ML) algorithms are being incorporated into many systems since they can learn and solve complex problems. Some of these systems can be considered as Safety-Critical Systems (SCS), therefore, the performance of ML algorithms should be sufficiently safe concerning the safety requirements of the incorporating SCS. However, the performance analysis of ML algorithms, usually, relies on metrics that were not developed with safety in mind. Accordingly, they may not be appropriate for assessing the performance of ML algorithms concerning safety. This paper debates on accounting for the distribution - not just the amount - of False Negatives as an additional element to be used when assessing ML algorithms to be integrated into SCS. We empirically try to assess the properness of incorporating ML-based components (anomaly-based intrusion detectors) into SCS using both traditional and novel SSPr and NPr metrics that focus on the numbers as well as the distribution of False Negatives. Results obtained by our experiment allow discussing the potential of ML-based components to be incorporated into SCS.
Flore (Florence Rese... arrow_drop_down Flore (Florence Research Repository)Conference object . 2021Data sources: Flore (Florence Research Repository)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1145/3412841.3442074&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu4 citations 4 popularity Top 10% influence Average impulse Average Powered by BIP!more_vert Flore (Florence Rese... arrow_drop_down Flore (Florence Research Repository)Conference object . 2021Data sources: Flore (Florence Research Repository)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1145/3412841.3442074&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Conference object , Preprint , Article 2021 ItalyPublisher:IEEE Kieu M.; Berlincioni L.; Galteri L.; Bertini M.; Bagdanov A. D.; Bimbo A. D.;handle: 2158/1245005
In this paper we propose a method for improving pedestrian detection in the thermal domain using two stages: first, a generative data augmentation approach is used, then a domain adaptation method using generated data adapts an RGB pedestrian detector. Our model, based on the Least-Squares Generative Adversarial Network, is trained to synthesize realistic thermal versions of input RGB images which are then used to augment the limited amount of labeled thermal pedestrian images available for training. We apply our generative data augmentation strategy in order to adapt a pretrained YOLOv3 pedestrian detector to detection in the thermal-only domain. Experimental results demonstrate the effectiveness of our approach: using less than 50\% of available real thermal training data, and relying on synthesized data generated by our model in the domain adaptation phase, our detector achieves state-of-the-art results on the KAIST Multispectral Pedestrian Detection Benchmark; even if more real thermal data is available adding GAN generated images to the training data results in improved performance, thus showing that these images act as an effective form of data augmentation. To the best of our knowledge, our detector achieves the best single-modality detection results on KAIST with respect to the state-of-the-art. Comment: Accepted at ICPR2020
Flore (Florence Rese... arrow_drop_down Flore (Florence Research Repository)Conference object . 2020Data sources: Flore (Florence Research Repository)Flore (Florence Research Repository)Conference object . 2020Data sources: Flore (Florence Research Repository)https://doi.org/10.1109/icpr48...Conference object . 2021 . Peer-reviewedLicense: IEEE CopyrightData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/icpr48806.2021.9412764&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 1 citations 1 popularity Average influence Average impulse Average Powered by BIP!more_vert Flore (Florence Rese... arrow_drop_down Flore (Florence Research Repository)Conference object . 2020Data sources: Flore (Florence Research Repository)Flore (Florence Research Repository)Conference object . 2020Data sources: Flore (Florence Research Repository)https://doi.org/10.1109/icpr48...Conference object . 2021 . Peer-reviewedLicense: IEEE CopyrightData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/icpr48806.2021.9412764&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Conference object , Preprint , Article 2021 ItalyPublisher:IEEE Authors: Innocenti S. U.; Becattini F.; Pernici F.; Del Bimbo A.;Innocenti S. U.; Becattini F.; Pernici F.; Del Bimbo A.;handle: 11365/1225613 , 2158/1243137
In this paper we present an event aggregation strategy to convert the output of an event camera into frames processable by traditional Computer Vision algorithms. The proposed method first generates sequences of intermediate binary representations, which are then losslessly transformed into a compact format by simply applying a binary-to-decimal conversion. This strategy allows us to encode temporal information directly into pixel values, which are then interpreted by deep learning models. We apply our strategy, called Temporal Binary Representation, to the task of Gesture Recognition, obtaining state of the art results on the popular DVS128 Gesture Dataset. To underline the effectiveness of the proposed method compared to existing ones, we also collect an extension of the dataset under more challenging conditions on which to perform experiments. Accepted at ICPR2020
arXiv.org e-Print Ar... arrow_drop_down Usiena air - Università di SienaConference object . 2020Data sources: Usiena air - Università di Sienahttps://doi.org/10.1109/icpr48...Conference object . 2021 . Peer-reviewedLicense: IEEE CopyrightData sources: CrossrefUsiena air - Università di SienaConference object . 2020Data sources: Usiena air - Università di SienaFlore (Florence Research Repository)Conference object . 2020Data sources: Flore (Florence Research Repository)https://doi.org/10.48550/arxiv...Article . 2020License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/icpr48806.2021.9412991&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 12 citations 12 popularity Top 10% influence Average impulse Top 10% Powered by BIP!more_vert arXiv.org e-Print Ar... arrow_drop_down Usiena air - Università di SienaConference object . 2020Data sources: Usiena air - Università di Sienahttps://doi.org/10.1109/icpr48...Conference object . 2021 . Peer-reviewedLicense: IEEE CopyrightData sources: CrossrefUsiena air - Università di SienaConference object . 2020Data sources: Usiena air - Università di SienaFlore (Florence Research Repository)Conference object . 2020Data sources: Flore (Florence Research Repository)https://doi.org/10.48550/arxiv...Article . 2020License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/icpr48806.2021.9412991&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Conference object , Preprint , Article 2021 ItalyPublisher:IEEE Authors: Ferrari, C.; Berlincioni, L.; Bertini, M.; Del Bimbo, A.;Ferrari, C.; Berlincioni, L.; Bertini, M.; Del Bimbo, A.;handle: 2158/1245004
In this paper, we propose an automatic approach for localizing the inner eye canthus in thermal face images. We first coarsely detect 5 facial keypoints corresponding to the center of the eyes, the nosetip and the ears. Then we compute a sparse 2D-3D points correspondence using a 3D Morphable Face Model (3DMM). This correspondence is used to project the entire 3D face onto the image, and subsequently locate the inner eye canthus. Detecting this location allows to obtain the most precise body temperature measurement for a person using a thermal camera. We evaluated the approach on a thermal face dataset provided with manually annotated landmarks. However, such manual annotations are normally conceived to identify facial parts such as eyes, nose and mouth, and are not specifically tailored for localizing the eye canthus region. As additional contribution, we enrich the original dataset by using the annotated landmarks to deform and project the 3DMM onto the images. Then, by manually selecting a small region corresponding to the eye canthus, we enrich the dataset with additional annotations. By using the manual landmarks, we ensure the correctness of the 3DMM projection, which can be used as ground-truth for future evaluations. Moreover, we supply the dataset with the 3D head poses and per-point visibility masks for detecting self-occlusions. The data is publicly available at https://www.micc.unifi.it/resources/datasets/thermal-face/.
Flore (Florence Rese... arrow_drop_down Flore (Florence Research Repository)Conference object . 2020Data sources: Flore (Florence Research Repository)Flore (Florence Research Repository)Conference object . 2020Data sources: Flore (Florence Research Repository)https://doi.org/10.1109/icpr48...Conference object . 2021 . Peer-reviewedLicense: IEEE CopyrightData sources: Crossrefhttps://doi.org/10.48550/arxiv...Article . 2020License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/icpr48806.2021.9412015&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 4 citations 4 popularity Top 10% influence Average impulse Average Powered by BIP!more_vert Flore (Florence Rese... arrow_drop_down Flore (Florence Research Repository)Conference object . 2020Data sources: Flore (Florence Research Repository)Flore (Florence Research Repository)Conference object . 2020Data sources: Flore (Florence Research Repository)https://doi.org/10.1109/icpr48...Conference object . 2021 . Peer-reviewedLicense: IEEE CopyrightData sources: Crossrefhttps://doi.org/10.48550/arxiv...Article . 2020License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/icpr48806.2021.9412015&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Conference object , Preprint 2021 ItalyPublisher:IEEE Authors: Zappardino, Fabio; Uricchio, Tiberio; Seidenari, Lorenzo; del Bimbo, Alberto;Zappardino, Fabio; Uricchio, Tiberio; Seidenari, Lorenzo; del Bimbo, Alberto;handle: 2158/1237257
To understand human behavior we must not just recognize individual actions but model possibly complex group activity and interactions. Hierarchical models obtain the best results in group activity recognition but require fine grained individual action annotations at the actor level. In this paper we show that using only skeletal data we can train a state-of-the art end-to-end system using only group activity labels at the sequence level. Our experiments show that models trained without individual action supervision perform poorly. On the other hand we show that pseudo-labels can be computed from any pre-trained feature extractor with comparable final performance. Finally our carefully designed lean pose only architecture shows highly competitive results versus more complex multimodal approaches even in the self-supervised variant. ICPR 2020
http://arxiv.org/pdf... arrow_drop_down Flore (Florence Research Repository); Archivio istituzionale della ricerca - Università di MacerataConference object . 2021Archivio istituzionale della ricerca - Università di MacerataConference object . 2021https://doi.org/10.1109/icpr48...Conference object . 2021 . Peer-reviewedLicense: IEEE CopyrightData sources: Crossrefhttps://doi.org/10.48550/arxiv...Article . 2021License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/icpr48806.2021.9413195&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 1 citations 1 popularity Average influence Average impulse Average Powered by BIP!more_vert http://arxiv.org/pdf... arrow_drop_down Flore (Florence Research Repository); Archivio istituzionale della ricerca - Università di MacerataConference object . 2021Archivio istituzionale della ricerca - Università di MacerataConference object . 2021https://doi.org/10.1109/icpr48...Conference object . 2021 . Peer-reviewedLicense: IEEE CopyrightData sources: Crossrefhttps://doi.org/10.48550/arxiv...Article . 2021License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/icpr48806.2021.9413195&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Other literature type , Conference object , Article 2021 Netherlands, Netherlands, ItalyPublisher:EDP Sciences Alessandro Mati; Marco Buffi; Stefano Dell’Orco; M.P. Ruiz Ramiro; S.R.A. Kersten; David Chiaramonti;The quality of biocrudes from fast pyrolysis of lignocellulosic biomass can be improved by optimizing the downstream condensation systems to separate and concentrate selected classes of compounds, thus operating different technological solutions and condensation temperatures in multiple condensation stages. Scientific literature reports that fractional condensation can be deployed as an effective and relatively affordable step in fast pyrolysis. It consists in a controlled multiple condensation approach, which aims at the separated collection of classes of compounds that can be further upgraded to bio-derived chemicals through downstream treatments. In this study, fractional condensation has been applied to a fast pyrolysis reactor of 1 kg h-1 feed, connected to two different condensation units: one composed by a series of two spray condensers and an intensive cooler; a second by an electrostatic precipitator and an intensive cooler too. Fast pyrolysis of pinewood was conducted in a bubbling fluidized bed reactor at 500 °C, while condensable vapours were collected by an interchangeable series of condensers. Using the first configuration, high boiling point compounds – such as sugars and lignin-derived oligomers – were condensed at higher temperatures in the first stage (100 – 170 °C), while water soluble lighter compounds and most of the water were condensed at lower temperatures and so largely removed from the bio-oil. In the first two condensing stages, the bio-oil water content remained below 7 wt % (resulting in 20 MJ kg-1 of energy content) maintaining about 43% of the liquid yield, compared to the 55% of the single step condensation runs. The work thus generated promising results, confirming the interest on upscaling the fractional condensation approach to full scale biorefinering.
http://www.scopus.co... arrow_drop_down Flore (Florence Research Repository)Conference object . 2021Data sources: Flore (Florence Research Repository)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1051/e3sconf/202123801009&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesgold 0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert http://www.scopus.co... arrow_drop_down Flore (Florence Research Repository)Conference object . 2021Data sources: Flore (Florence Research Repository)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1051/e3sconf/202123801009&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Part of book or chapter of book , Conference object 2021 ItalyPublisher:Springer International Publishing Authors: Capecchi, I.; Grilli, G.; Barbierato, E.; Sacchelli, S.;Capecchi, I.; Grilli, G.; Barbierato, E.; Sacchelli, S.;AbstractA solution to cope with chaotic urban settlements and frenetic everyday life is refuging in nature as a way to reduce stress. In general—in recent years—it has been scientifically demonstrated how natural areas are an important environment for psycho-physiological health. As a consequence, it is important to plan dedicated spaces for stress recovery in order to increase the well-being of people. With respect to forests, there is a growing interest in understanding the marketing and tourist potential of forest-therapy activities and policies. This paper develops a decision support system (DSS) for decision makers, based on geographic information system to define the suitability of forest areas to improve psychological and physiological human well-being. Innovative technologies such as electroencephalography (EEG) and virtual reality (VR) are applied to test human status. The DSS combines four sets of indicators in a multi-attribute decision analysis and identifies the areas with the largest stress-recovery potential. Two multi-attribute model—one in summer and one in winter—are elaborated to obtain a dynamic evaluation of suitability. Results show significant differences among forest type, forest management, altitude range, and season in terms of stand suitability. EEG and VR seem to be promising technologies in this research area. Strengths and weaknesses of the approach, as well as potential future improvement and implications for territorial marketing, are suggested.
https://link.springe... arrow_drop_down https://link.springer.com/cont...Part of book or chapter of bookLicense: CC BYData sources: UnpayWallArchivio istituzionale della ricerca - Università di PadovaConference object . 2021Flore (Florence Research Repository)Conference object . 2021Data sources: Flore (Florence Research Repository)https://doi.org/10.1007/978-3-...Part of book or chapter of book . 2021 . Peer-reviewedLicense: CC BYData sources: CrossrefArchivio istituzionale della ricerca - Università di PadovaPart of book or chapter of book . 2021add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1007/978-3-030-57764-3_12&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routeshybrid 1 citations 1 popularity Average influence Average impulse Average Powered by BIP!more_vert https://link.springe... arrow_drop_down https://link.springer.com/cont...Part of book or chapter of bookLicense: CC BYData sources: UnpayWallArchivio istituzionale della ricerca - Università di PadovaConference object . 2021Flore (Florence Research Repository)Conference object . 2021Data sources: Flore (Florence Research Repository)https://doi.org/10.1007/978-3-...Part of book or chapter of book . 2021 . Peer-reviewedLicense: CC BYData sources: CrossrefArchivio istituzionale della ricerca - Università di PadovaPart of book or chapter of book . 2021add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1007/978-3-030-57764-3_12&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Conference object 2021 ItalyPublisher:International Association for Bridge and Structural Engineering (IABSE) Authors: Gloria Terenzi; Iacopo Costoli; Stefano Sorace;Gloria Terenzi; Iacopo Costoli; Stefano Sorace;handle: 11368/3001883 , 2158/1202063
<p>A school building with reinforced concrete structure, seismically retrofitted in 2013 and damaged by the 2016 Central Italy earthquake, is examined in this paper. A time-history assessment analysis is initially carried out in pre-rehabilitated conditions by simulating also the presence of the clay brick masonry infill perimeter walls and partitions in the finite element model of the structure. Based on the results of this analysis, a different retrofit solution is proposed, consisting in the incorporation of dissipative braces equipped with pressurized fluid viscous dampers. The verification analyses developed in this new configuration for the main shock records of the 2016 earthquake highlight slightly damaged and easily repairable response conditions of a little number of partitions — instead of the diffused moderate-to-severe damage surveyed in the building internal and perimeter infills</p><p>— and an elastic response of structural members.</p>
Flore (Florence Rese... arrow_drop_down Flore (Florence Research Repository)Conference object . 2020Full-Text: https://flore.unifi.it/bitstream/2158/1202063/1/IABSE%20Conference%20Christchurch%202020.pdfData sources: Flore (Florence Research Repository)ArTS - Archivio della ricerca dell' Università degli Studi di TriesteConference object . 2020Archivio istituzionale della ricerca - Università degli Studi di UdineConference object . 2020Archivio istituzionale della ricerca - Università di TriesteConference object . 2020add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euAccess Routesbronze 0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert Flore (Florence Rese... arrow_drop_down Flore (Florence Research Repository)Conference object . 2020Full-Text: https://flore.unifi.it/bitstream/2158/1202063/1/IABSE%20Conference%20Christchurch%202020.pdfData sources: Flore (Florence Research Repository)ArTS - Archivio della ricerca dell' Università degli Studi di TriesteConference object . 2020Archivio istituzionale della ricerca - Università degli Studi di UdineConference object . 2020Archivio istituzionale della ricerca - Università di TriesteConference object . 2020add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.2749/christchurch.2021.0605&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Conference object , Preprint 2020 France, ItalyPublisher:IEEE Ettore Maria Celozzi; Luca Ciabini; Luca Cultrera; Pietro Pala; Stefano Berretti; Mohamed Daoudi; Alberto Del Bimbo;handle: 2158/1223027
In this paper, a model is presented to extract statistical summaries to characterize the repetition of a cyclic body action, for instance a gym exercise, for the purpose of checking the compliance of the observed action to a template one and highlighting the parts of the action that are not correctly executed (if any). The proposed system relies on a Riemannian metric to compute the distance between two poses in such a way that the geometry of the manifold where the pose descriptors lie is preserved; a model to detect the begin and end of each cycle; a model to temporally align the poses of different cycles so as to accurately estimate the \emph{cross-sectional} mean and variance of poses across different cycles. The proposed model is demonstrated using gym videos taken from the Internet. Comment: accepted at 15th IEEE International Conference on Automatic Face and Gesture Recognition 2020
arXiv.org e-Print Ar... arrow_drop_down Flore (Florence Research Repository)Conference object . 2020Data sources: Flore (Florence Research Repository)https://doi.org/10.1109/fg4788...Conference object . 2020 . Peer-reviewedLicense: IEEE CopyrightData sources: CrossrefMémoires en Sciences de l'Information et de la CommunicationConference object . 2020Full-Text: https://hal.science/hal-02863164/documenthttps://doi.org/10.48550/arxiv...Article . 2020License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euAccess RoutesGreen 0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert arXiv.org e-Print Ar... arrow_drop_down Flore (Florence Research Repository)Conference object . 2020Data sources: Flore (Florence Research Repository)https://doi.org/10.1109/fg4788...Conference object . 2020 . Peer-reviewedLicense: IEEE CopyrightData sources: CrossrefMémoires en Sciences de l'Information et de la CommunicationConference object . 2020Full-Text: https://hal.science/hal-02863164/documenthttps://doi.org/10.48550/arxiv...Article . 2020License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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description Publicationkeyboard_double_arrow_right Article , Conference object 2021 ItalyPublisher:MDPI AG Authors: Chiara Giola; Piero Danti; Sandro Magnani;Chiara Giola; Piero Danti; Sandro Magnani;In the age of AI, companies strive to extract benefits from data. In the first steps of data analysis, an arduous dilemma scientists have to cope with is the definition of the ’right’ quantity of data needed for a certain task. In particular, when dealing with energy management, one of the most thriving application of AI is the consumption’s optimization of energy plant generators. When designing a strategy to improve the generators’ schedule, a piece of essential information is the future energy load requested by the plant. This topic, in the literature it is referred to as load forecasting, has lately gained great popularity; in this paper authors underline the problem of estimating the correct size of data to train prediction algorithms and propose a suitable methodology. The main characters of this methodology are the Learning Curves, a powerful tool to track algorithms performance whilst data training-set size varies. At first, a brief review of the state of the art and a shallow analysis of eligible machine learning techniques are offered. Furthermore, the hypothesis and constraints of the work are explained, presenting the dataset and the goal of the analysis. Finally, the methodology is elucidated and the results are discussed.
Flore (Florence Rese... arrow_drop_down Flore (Florence Research Repository)Article . 2021Data sources: Flore (Florence Research Repository)https://doi.org/10.3390/engpro...Conference object . 2021 . Peer-reviewedLicense: CC BYData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/engproc2021005038&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routeshybrid 1 citations 1 popularity Average influence Average impulse Average Powered by BIP!more_vert Flore (Florence Rese... arrow_drop_down Flore (Florence Research Repository)Article . 2021Data sources: Flore (Florence Research Repository)https://doi.org/10.3390/engpro...Conference object . 2021 . Peer-reviewedLicense: CC BYData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/engproc2021005038&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Conference object 2021 ItalyPublisher:ACM Authors: Gharib M.; Zoppi T.; Bondavalli A.;Gharib M.; Zoppi T.; Bondavalli A.;handle: 2158/1238469
Nowadays, Machine Learning (ML) algorithms are being incorporated into many systems since they can learn and solve complex problems. Some of these systems can be considered as Safety-Critical Systems (SCS), therefore, the performance of ML algorithms should be sufficiently safe concerning the safety requirements of the incorporating SCS. However, the performance analysis of ML algorithms, usually, relies on metrics that were not developed with safety in mind. Accordingly, they may not be appropriate for assessing the performance of ML algorithms concerning safety. This paper debates on accounting for the distribution - not just the amount - of False Negatives as an additional element to be used when assessing ML algorithms to be integrated into SCS. We empirically try to assess the properness of incorporating ML-based components (anomaly-based intrusion detectors) into SCS using both traditional and novel SSPr and NPr metrics that focus on the numbers as well as the distribution of False Negatives. Results obtained by our experiment allow discussing the potential of ML-based components to be incorporated into SCS.
Flore (Florence Rese... arrow_drop_down Flore (Florence Research Repository)Conference object . 2021Data sources: Flore (Florence Research Repository)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1145/3412841.3442074&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu4 citations 4 popularity Top 10% influence Average impulse Average Powered by BIP!more_vert Flore (Florence Rese... arrow_drop_down Flore (Florence Research Repository)Conference object . 2021Data sources: Flore (Florence Research Repository)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1145/3412841.3442074&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Conference object , Preprint , Article 2021 ItalyPublisher:IEEE Kieu M.; Berlincioni L.; Galteri L.; Bertini M.; Bagdanov A. D.; Bimbo A. D.;handle: 2158/1245005
In this paper we propose a method for improving pedestrian detection in the thermal domain using two stages: first, a generative data augmentation approach is used, then a domain adaptation method using generated data adapts an RGB pedestrian detector. Our model, based on the Least-Squares Generative Adversarial Network, is trained to synthesize realistic thermal versions of input RGB images which are then used to augment the limited amount of labeled thermal pedestrian images available for training. We apply our generative data augmentation strategy in order to adapt a pretrained YOLOv3 pedestrian detector to detection in the thermal-only domain. Experimental results demonstrate the effectiveness of our approach: using less than 50\% of available real thermal training data, and relying on synthesized data generated by our model in the domain adaptation phase, our detector achieves state-of-the-art results on the KAIST Multispectral Pedestrian Detection Benchmark; even if more real thermal data is available adding GAN generated images to the training data results in improved performance, thus showing that these images act as an effective form of data augmentation. To the best of our knowledge, our detector achieves the best single-modality detection results on KAIST with respect to the state-of-the-art. Comment: Accepted at ICPR2020
Flore (Florence Rese... arrow_drop_down Flore (Florence Research Repository)Conference object . 2020Data sources: Flore (Florence Research Repository)Flore (Florence Research Repository)Conference object . 2020Data sources: Flore (Florence Research Repository)https://doi.org/10.1109/icpr48...Conference object . 2021 . Peer-reviewedLicense: IEEE CopyrightData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/icpr48806.2021.9412764&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 1 citations 1 popularity Average influence Average impulse Average Powered by BIP!more_vert Flore (Florence Rese... arrow_drop_down Flore (Florence Research Repository)Conference object . 2020Data sources: Flore (Florence Research Repository)Flore (Florence Research Repository)Conference object . 2020Data sources: Flore (Florence Research Repository)https://doi.org/10.1109/icpr48...Conference object . 2021 . Peer-reviewedLicense: IEEE CopyrightData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/icpr48806.2021.9412764&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Conference object , Preprint , Article 2021 ItalyPublisher:IEEE Authors: Innocenti S. U.; Becattini F.; Pernici F.; Del Bimbo A.;Innocenti S. U.; Becattini F.; Pernici F.; Del Bimbo A.;handle: 11365/1225613 , 2158/1243137
In this paper we present an event aggregation strategy to convert the output of an event camera into frames processable by traditional Computer Vision algorithms. The proposed method first generates sequences of intermediate binary representations, which are then losslessly transformed into a compact format by simply applying a binary-to-decimal conversion. This strategy allows us to encode temporal information directly into pixel values, which are then interpreted by deep learning models. We apply our strategy, called Temporal Binary Representation, to the task of Gesture Recognition, obtaining state of the art results on the popular DVS128 Gesture Dataset. To underline the effectiveness of the proposed method compared to existing ones, we also collect an extension of the dataset under more challenging conditions on which to perform experiments. Accepted at ICPR2020
arXiv.org e-Print Ar... arrow_drop_down Usiena air - Università di SienaConference object . 2020Data sources: Usiena air - Università di Sienahttps://doi.org/10.1109/icpr48...Conference object . 2021 . Peer-reviewedLicense: IEEE CopyrightData sources: CrossrefUsiena air - Università di SienaConference object . 2020Data sources: Usiena air - Università di SienaFlore (Florence Research Repository)Conference object . 2020Data sources: Flore (Florence Research Repository)https://doi.org/10.48550/arxiv...Article . 2020License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/icpr48806.2021.9412991&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 12 citations 12 popularity Top 10% influence Average impulse Top 10% Powered by BIP!more_vert arXiv.org e-Print Ar... arrow_drop_down Usiena air - Università di SienaConference object . 2020Data sources: Usiena air - Università di Sienahttps://doi.org/10.1109/icpr48...Conference object . 2021 . Peer-reviewedLicense: IEEE CopyrightData sources: CrossrefUsiena air - Università di SienaConference object . 2020Data sources: Usiena air - Università di SienaFlore (Florence Research Repository)Conference object . 2020Data sources: Flore (Florence Research Repository)https://doi.org/10.48550/arxiv...Article . 2020License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/icpr48806.2021.9412991&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Conference object , Preprint , Article 2021 ItalyPublisher:IEEE Authors: Ferrari, C.; Berlincioni, L.; Bertini, M.; Del Bimbo, A.;Ferrari, C.; Berlincioni, L.; Bertini, M.; Del Bimbo, A.;handle: 2158/1245004
In this paper, we propose an automatic approach for localizing the inner eye canthus in thermal face images. We first coarsely detect 5 facial keypoints corresponding to the center of the eyes, the nosetip and the ears. Then we compute a sparse 2D-3D points correspondence using a 3D Morphable Face Model (3DMM). This correspondence is used to project the entire 3D face onto the image, and subsequently locate the inner eye canthus. Detecting this location allows to obtain the most precise body temperature measurement for a person using a thermal camera. We evaluated the approach on a thermal face dataset provided with manually annotated landmarks. However, such manual annotations are normally conceived to identify facial parts such as eyes, nose and mouth, and are not specifically tailored for localizing the eye canthus region. As additional contribution, we enrich the original dataset by using the annotated landmarks to deform and project the 3DMM onto the images. Then, by manually selecting a small region corresponding to the eye canthus, we enrich the dataset with additional annotations. By using the manual landmarks, we ensure the correctness of the 3DMM projection, which can be used as ground-truth for future evaluations. Moreover, we supply the dataset with the 3D head poses and per-point visibility masks for detecting self-occlusions. The data is publicly available at https://www.micc.unifi.it/resources/datasets/thermal-face/.
Flore (Florence Rese... arrow_drop_down Flore (Florence Research Repository)Conference object . 2020Data sources: Flore (Florence Research Repository)Flore (Florence Research Repository)Conference object . 2020Data sources: Flore (Florence Research Repository)https://doi.org/10.1109/icpr48...Conference object . 2021 . Peer-reviewedLicense: IEEE CopyrightData sources: Crossrefhttps://doi.org/10.48550/arxiv...Article . 2020License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euAccess RoutesGreen 4 citations 4 popularity Top 10% influence Average impulse Average Powered by BIP!more_vert Flore (Florence Rese... arrow_drop_down Flore (Florence Research Repository)Conference object . 2020Data sources: Flore (Florence Research Repository)Flore (Florence Research Repository)Conference object . 2020Data sources: Flore (Florence Research Repository)https://doi.org/10.1109/icpr48...Conference object . 2021 . Peer-reviewedLicense: IEEE CopyrightData sources: Crossrefhttps://doi.org/10.48550/arxiv...Article . 2020License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/icpr48806.2021.9412015&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Conference object , Preprint 2021 ItalyPublisher:IEEE Authors: Zappardino, Fabio; Uricchio, Tiberio; Seidenari, Lorenzo; del Bimbo, Alberto;Zappardino, Fabio; Uricchio, Tiberio; Seidenari, Lorenzo; del Bimbo, Alberto;handle: 2158/1237257
To understand human behavior we must not just recognize individual actions but model possibly complex group activity and interactions. Hierarchical models obtain the best results in group activity recognition but require fine grained individual action annotations at the actor level. In this paper we show that using only skeletal data we can train a state-of-the art end-to-end system using only group activity labels at the sequence level. Our experiments show that models trained without individual action supervision perform poorly. On the other hand we show that pseudo-labels can be computed from any pre-trained feature extractor with comparable final performance. Finally our carefully designed lean pose only architecture shows highly competitive results versus more complex multimodal approaches even in the self-supervised variant. ICPR 2020
http://arxiv.org/pdf... arrow_drop_down Flore (Florence Research Repository); Archivio istituzionale della ricerca - Università di MacerataConference object . 2021Archivio istituzionale della ricerca - Università di MacerataConference object . 2021https://doi.org/10.1109/icpr48...Conference object . 2021 . Peer-reviewedLicense: IEEE CopyrightData sources: Crossrefhttps://doi.org/10.48550/arxiv...Article . 2021License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/icpr48806.2021.9413195&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 1 citations 1 popularity Average influence Average impulse Average Powered by BIP!more_vert http://arxiv.org/pdf... arrow_drop_down Flore (Florence Research Repository); Archivio istituzionale della ricerca - Università di MacerataConference object . 2021Archivio istituzionale della ricerca - Università di MacerataConference object . 2021https://doi.org/10.1109/icpr48...Conference object . 2021 . Peer-reviewedLicense: IEEE CopyrightData sources: Crossrefhttps://doi.org/10.48550/arxiv...Article . 2021License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/icpr48806.2021.9413195&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Other literature type , Conference object , Article 2021 Netherlands, Netherlands, ItalyPublisher:EDP Sciences Alessandro Mati; Marco Buffi; Stefano Dell’Orco; M.P. Ruiz Ramiro; S.R.A. Kersten; David Chiaramonti;The quality of biocrudes from fast pyrolysis of lignocellulosic biomass can be improved by optimizing the downstream condensation systems to separate and concentrate selected classes of compounds, thus operating different technological solutions and condensation temperatures in multiple condensation stages. Scientific literature reports that fractional condensation can be deployed as an effective and relatively affordable step in fast pyrolysis. It consists in a controlled multiple condensation approach, which aims at the separated collection of classes of compounds that can be further upgraded to bio-derived chemicals through downstream treatments. In this study, fractional condensation has been applied to a fast pyrolysis reactor of 1 kg h-1 feed, connected to two different condensation units: one composed by a series of two spray condensers and an intensive cooler; a second by an electrostatic precipitator and an intensive cooler too. Fast pyrolysis of pinewood was conducted in a bubbling fluidized bed reactor at 500 °C, while condensable vapours were collected by an interchangeable series of condensers. Using the first configuration, high boiling point compounds – such as sugars and lignin-derived oligomers – were condensed at higher temperatures in the first stage (100 – 170 °C), while water soluble lighter compounds and most of the water were condensed at lower temperatures and so largely removed from the bio-oil. In the first two condensing stages, the bio-oil water content remained below 7 wt % (resulting in 20 MJ kg-1 of energy content) maintaining about 43% of the liquid yield, compared to the 55% of the single step condensation runs. The work thus generated promising results, confirming the interest on upscaling the fractional condensation approach to full scale biorefinering.
http://www.scopus.co... arrow_drop_down Flore (Florence Research Repository)Conference object . 2021Data sources: Flore (Florence Research Repository)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1051/e3sconf/202123801009&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesgold 0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert http://www.scopus.co... arrow_drop_down Flore (Florence Research Repository)Conference object . 2021Data sources: Flore (Florence Research Repository)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1051/e3sconf/202123801009&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Part of book or chapter of book , Conference object 2021 ItalyPublisher:Springer International Publishing Authors: Capecchi, I.; Grilli, G.; Barbierato, E.; Sacchelli, S.;Capecchi, I.; Grilli, G.; Barbierato, E.; Sacchelli, S.;AbstractA solution to cope with chaotic urban settlements and frenetic everyday life is refuging in nature as a way to reduce stress. In general—in recent years—it has been scientifically demonstrated how natural areas are an important environment for psycho-physiological health. As a consequence, it is important to plan dedicated spaces for stress recovery in order to increase the well-being of people. With respect to forests, there is a growing interest in understanding the marketing and tourist potential of forest-therapy activities and policies. This paper develops a decision support system (DSS) for decision makers, based on geographic information system to define the suitability of forest areas to improve psychological and physiological human well-being. Innovative technologies such as electroencephalography (EEG) and virtual reality (VR) are applied to test human status. The DSS combines four sets of indicators in a multi-attribute decision analysis and identifies the areas with the largest stress-recovery potential. Two multi-attribute model—one in summer and one in winter—are elaborated to obtain a dynamic evaluation of suitability. Results show significant differences among forest type, forest management, altitude range, and season in terms of stand suitability. EEG and VR seem to be promising technologies in this research area. Strengths and weaknesses of the approach, as well as potential future improvement and implications for territorial marketing, are suggested.
https://link.springe... arrow_drop_down https://link.springer.com/cont...Part of book or chapter of bookLicense: CC BYData sources: UnpayWallArchivio istituzionale della ricerca - Università di PadovaConference object . 2021Flore (Florence Research Repository)Conference object . 2021Data sources: Flore (Florence Research Repository)https://doi.org/10.1007/978-3-...Part of book or chapter of book . 2021 . Peer-reviewedLicense: CC BYData sources: CrossrefArchivio istituzionale della ricerca - Università di PadovaPart of book or chapter of book . 2021add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1007/978-3-030-57764-3_12&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routeshybrid 1 citations 1 popularity Average influence Average impulse Average Powered by BIP!more_vert https://link.springe... arrow_drop_down https://link.springer.com/cont...Part of book or chapter of bookLicense: CC BYData sources: UnpayWallArchivio istituzionale della ricerca - Università di PadovaConference object . 2021Flore (Florence Research Repository)Conference object . 2021Data sources: Flore (Florence Research Repository)https://doi.org/10.1007/978-3-...Part of book or chapter of book . 2021 . Peer-reviewedLicense: CC BYData sources: CrossrefArchivio istituzionale della ricerca - Università di PadovaPart of book or chapter of book . 2021add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1007/978-3-030-57764-3_12&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Conference object 2021 ItalyPublisher:International Association for Bridge and Structural Engineering (IABSE) Authors: Gloria Terenzi; Iacopo Costoli; Stefano Sorace;Gloria Terenzi; Iacopo Costoli; Stefano Sorace;handle: 11368/3001883 , 2158/1202063
<p>A school building with reinforced concrete structure, seismically retrofitted in 2013 and damaged by the 2016 Central Italy earthquake, is examined in this paper. A time-history assessment analysis is initially carried out in pre-rehabilitated conditions by simulating also the presence of the clay brick masonry infill perimeter walls and partitions in the finite element model of the structure. Based on the results of this analysis, a different retrofit solution is proposed, consisting in the incorporation of dissipative braces equipped with pressurized fluid viscous dampers. The verification analyses developed in this new configuration for the main shock records of the 2016 earthquake highlight slightly damaged and easily repairable response conditions of a little number of partitions — instead of the diffused moderate-to-severe damage surveyed in the building internal and perimeter infills</p><p>— and an elastic response of structural members.</p>
Flore (Florence Rese... arrow_drop_down Flore (Florence Research Repository)Conference object . 2020Full-Text: https://flore.unifi.it/bitstream/2158/1202063/1/IABSE%20Conference%20Christchurch%202020.pdfData sources: Flore (Florence Research Repository)ArTS - Archivio della ricerca dell' Università degli Studi di TriesteConference object . 2020Archivio istituzionale della ricerca - Università degli Studi di UdineConference object . 2020Archivio istituzionale della ricerca - Università di TriesteConference object . 2020add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.2749/christchurch.2021.0605&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesbronze 0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert Flore (Florence Rese... arrow_drop_down Flore (Florence Research Repository)Conference object . 2020Full-Text: https://flore.unifi.it/bitstream/2158/1202063/1/IABSE%20Conference%20Christchurch%202020.pdfData sources: Flore (Florence Research Repository)ArTS - Archivio della ricerca dell' Università degli Studi di TriesteConference object . 2020Archivio istituzionale della ricerca - Università degli Studi di UdineConference object . 2020Archivio istituzionale della ricerca - Università di TriesteConference object . 2020add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.2749/christchurch.2021.0605&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Conference object , Preprint 2020 France, ItalyPublisher:IEEE Ettore Maria Celozzi; Luca Ciabini; Luca Cultrera; Pietro Pala; Stefano Berretti; Mohamed Daoudi; Alberto Del Bimbo;handle: 2158/1223027
In this paper, a model is presented to extract statistical summaries to characterize the repetition of a cyclic body action, for instance a gym exercise, for the purpose of checking the compliance of the observed action to a template one and highlighting the parts of the action that are not correctly executed (if any). The proposed system relies on a Riemannian metric to compute the distance between two poses in such a way that the geometry of the manifold where the pose descriptors lie is preserved; a model to detect the begin and end of each cycle; a model to temporally align the poses of different cycles so as to accurately estimate the \emph{cross-sectional} mean and variance of poses across different cycles. The proposed model is demonstrated using gym videos taken from the Internet. Comment: accepted at 15th IEEE International Conference on Automatic Face and Gesture Recognition 2020
arXiv.org e-Print Ar... arrow_drop_down Flore (Florence Research Repository)Conference object . 2020Data sources: Flore (Florence Research Repository)https://doi.org/10.1109/fg4788...Conference object . 2020 . Peer-reviewedLicense: IEEE CopyrightData sources: CrossrefMémoires en Sciences de l'Information et de la CommunicationConference object . 2020Full-Text: https://hal.science/hal-02863164/documenthttps://doi.org/10.48550/arxiv...Article . 2020License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/fg47880.2020.00054&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert arXiv.org e-Print Ar... arrow_drop_down Flore (Florence Research Repository)Conference object . 2020Data sources: Flore (Florence Research Repository)https://doi.org/10.1109/fg4788...Conference object . 2020 . Peer-reviewedLicense: IEEE CopyrightData sources: CrossrefMémoires en Sciences de l'Information et de la CommunicationConference object . 2020Full-Text: https://hal.science/hal-02863164/documenthttps://doi.org/10.48550/arxiv...Article . 2020License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/fg47880.2020.00054&type=result"></script>'); --> </script>
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