2024
Journal article
Open Access
Training a shallow NN to erase ink seepage in historical manuscripts based on a degradation model
Savino P., Tonazzini A.
In historical recto-verso manuscripts, very often the text written on the opposite page of the folio penetrates through the fiber of the paper, so that the texts on the two sides appear mixed. This is a very impairing damage that cannot be physically removed, and hinders both the work of philologists and palaeographers and the automatic analysis of linguistic contents. A procedure based on neural networks (NN) is proposed here to clean up the complex background of the manuscripts from this interference. We adopt a very simple shallow NN whose learning phase employs a training set generated from the data itself using a theoretical blending model that takes into account ink diffusion and saturation. By virtue of the parametric nature of the model, various levels of damage can be simulated in the training set, favoring a generalization capability of the NN. More explicitly, the network can be trained without the need for a large class of other similar manuscripts, but is still able, at least to some extent, to classify manuscripts with varying degrees of corruption. We compare the performance of this NN and other methods both qualitatively and quantitatively on a reference dataset and heavily damaged historical manuscripts.Source: Neural computing & applications (Print) (2024). doi:10.1007/s00521-023-09354-7
DOI: 10.1007/s00521-023-09354-7
Metrics:
Savino P., Tonazzini A.
In historical recto-verso manuscripts, very often the text written on the opposite page of the folio penetrates through the fiber of the paper, so that the texts on the two sides appear mixed. This is a very impairing damage that cannot be physically removed, and hinders both the work of philologists and palaeographers and the automatic analysis of linguistic contents. A procedure based on neural networks (NN) is proposed here to clean up the complex background of the manuscripts from this interference. We adopt a very simple shallow NN whose learning phase employs a training set generated from the data itself using a theoretical blending model that takes into account ink diffusion and saturation. By virtue of the parametric nature of the model, various levels of damage can be simulated in the training set, favoring a generalization capability of the NN. More explicitly, the network can be trained without the need for a large class of other similar manuscripts, but is still able, at least to some extent, to classify manuscripts with varying degrees of corruption. We compare the performance of this NN and other methods both qualitatively and quantitatively on a reference dataset and heavily damaged historical manuscripts.Source: Neural computing & applications (Print) (2024). doi:10.1007/s00521-023-09354-7
DOI: 10.1007/s00521-023-09354-7
Metrics:
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| CNR ExploRA
2024
Other
Open Access
Strumenti innovativi per introdurre gli escursionisti ad una migliore lettura dell'ambiente
Ducci F., Dell'Orso R., Martinelli M.
Lo scorso 30 agosto 2023 il Club Alpino Italiano Regione Toscana, tramite il suo Comitato Scientifico Toscano, e l'Istituto di Scienza e Tecnologie dell'Informazione del Consiglio Nazionale delle Ricerche hanno stipulato un accordo di collaborazione per attività di ricerca volta a sviluppare strumenti innovativi per introdurre gli escursionisti ad una migliore lettura dell'ambiente.
Ducci F., Dell'Orso R., Martinelli M.
Lo scorso 30 agosto 2023 il Club Alpino Italiano Regione Toscana, tramite il suo Comitato Scientifico Toscano, e l'Istituto di Scienza e Tecnologie dell'Informazione del Consiglio Nazionale delle Ricerche hanno stipulato un accordo di collaborazione per attività di ricerca volta a sviluppare strumenti innovativi per introdurre gli escursionisti ad una migliore lettura dell'ambiente.
See at:
ISTI Repository | CNR ExploRA | www.loscarpone.cai.it
2024
Journal article
Restricted
Bending-reinforced grid shells for free-form architectural surfaces
Laccone F., Pietroni N., Cignoni P., Malomo L.
We introduce a new method for designing reinforcement for grid shells and improving their resistance to out-of-plane forces inducing bending. The central concept is to support the base network of elements with an additional layer of beams placed at a certain distance from the base surface. We exploit two main techniques to design these structures: first, we derive the orientation of the beam network on a given initial surface forming the grid shell to be reinforced; then, we compute the height of the additional layer that maximizes its overall structural performance. Our method includes a new formulation to derive a smooth direction field that orients the quad remeshing and a novel algorithm that iteratively optimizes the height of the additional layer to minimize the structure's compliance. We couple our optimization strategy with a set of constraints to improve buildability of the network and, simultaneously, preserve the initial surface. We showcase our method on a significant dataset of shapes to demonstrate its applicability to cases where free-form grid shells do not exhibit adequate structural performance due to their geometry.Source: Computer Aided Design 168 (2024). doi:10.1016/j.cad.2023.103670
DOI: 10.1016/j.cad.2023.103670
Metrics:
Laccone F., Pietroni N., Cignoni P., Malomo L.
We introduce a new method for designing reinforcement for grid shells and improving their resistance to out-of-plane forces inducing bending. The central concept is to support the base network of elements with an additional layer of beams placed at a certain distance from the base surface. We exploit two main techniques to design these structures: first, we derive the orientation of the beam network on a given initial surface forming the grid shell to be reinforced; then, we compute the height of the additional layer that maximizes its overall structural performance. Our method includes a new formulation to derive a smooth direction field that orients the quad remeshing and a novel algorithm that iteratively optimizes the height of the additional layer to minimize the structure's compliance. We couple our optimization strategy with a set of constraints to improve buildability of the network and, simultaneously, preserve the initial surface. We showcase our method on a significant dataset of shapes to demonstrate its applicability to cases where free-form grid shells do not exhibit adequate structural performance due to their geometry.Source: Computer Aided Design 168 (2024). doi:10.1016/j.cad.2023.103670
DOI: 10.1016/j.cad.2023.103670
Metrics:
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2024
Other
Open Access
SMTP smuggling
Gennai F.
Descrizione tecnica della vulnerabilità del sistema email Internet, denominata SMTP smuggling e resa pubblica in data 18 dicembre 2023 dalla società SEC Consult di Vienna.
Gennai F.
Descrizione tecnica della vulnerabilità del sistema email Internet, denominata SMTP smuggling e resa pubblica in data 18 dicembre 2023 dalla società SEC Consult di Vienna.
See at:
ISTI Repository | CNR ExploRA
2024
Journal article
Open Access
A comparative study of disabled people's experiences with the video conferencing tools Zoom, MS Teams, Google Meet and Skype
Hersh M., Leporini B., Buzzi M.
The paper presents a comparative mixed methods study of the accessibility and usability for disabled people of four video conferencing tools, Zoom, MS Teams, Google Meet and Skype. Useable responses were obtained from 81 disabled people with diverse characteristics, mainly in the UK, though some groups had low representation. None of the tools was considered fully accessible and useable. Zoom was both the most commonly used and the most frequently preferred (56.1%) tool, with MS Teams second in usage and a trailing second in preferences (15.9%). It was considered to have better captioning, but otherwise to generally be a poor second to Zoom. Skype was the most commonly used before lockdown, but was considered dated and its limited use was mainly social, whereas the other tools were also used in work and education. The results were used to draw up separate lists of recommendations for developers and meeting organisers and hosts, as the study also identified actions for organisers and hosts to improve meeting accessibility. Developer recommendations include several easy to set customisation and user friendly interface features, involving disabled people and specific accessibility features, including compatibility with assistive technology, keyboard shortcuts for all functions and automatically-on high quality captions.Source: Behaviour & information technology (Online) (2024). doi:10.1080/0144929X.2023.2286533
DOI: 10.1080/0144929x.2023.2286533
Metrics:
Hersh M., Leporini B., Buzzi M.
The paper presents a comparative mixed methods study of the accessibility and usability for disabled people of four video conferencing tools, Zoom, MS Teams, Google Meet and Skype. Useable responses were obtained from 81 disabled people with diverse characteristics, mainly in the UK, though some groups had low representation. None of the tools was considered fully accessible and useable. Zoom was both the most commonly used and the most frequently preferred (56.1%) tool, with MS Teams second in usage and a trailing second in preferences (15.9%). It was considered to have better captioning, but otherwise to generally be a poor second to Zoom. Skype was the most commonly used before lockdown, but was considered dated and its limited use was mainly social, whereas the other tools were also used in work and education. The results were used to draw up separate lists of recommendations for developers and meeting organisers and hosts, as the study also identified actions for organisers and hosts to improve meeting accessibility. Developer recommendations include several easy to set customisation and user friendly interface features, involving disabled people and specific accessibility features, including compatibility with assistive technology, keyboard shortcuts for all functions and automatically-on high quality captions.Source: Behaviour & information technology (Online) (2024). doi:10.1080/0144929X.2023.2286533
DOI: 10.1080/0144929x.2023.2286533
Metrics:
See at:
ISTI Repository | www.tandfonline.com | CNR ExploRA
2024
Journal article
Open Access
Deep learning and structural health monitoring: a TFT-based approach for anomaly detection in masonry towers
Falchi F., Girardi M., Gurioli G., Messina N., Padovani C., Pellegrini D.
Detecting anomalies in the vibrational features of age-old buildings is crucial within the Structural Health Monitoring (SHM) framework. The SHM techniques can leverage information from onsite measurements and environmental sources to identify the dynamic properties (such as the frequencies) of the monitored structure, searching for possible deviations or unusual behavior over time. In this paper, the Temporal Fusion Transformer (TFT) network, a deep learning algorithm initially designed for multi-horizon time series forecasting and tested on electricity, traffic, retail, and volatility problems, is applied to SHM. The TFT approach is adopted to investigate the behavior of the Guinigi Tower located in Lucca (Italy) and subjected to a long-term dynamic monitoring campaign. The TFT network is trained on the tower's experimental frequencies enriched with other environmental parameters. The transformer is then employed to predict the vibrational features (natural frequencies, root mean squares values of the velocity time series) and detect possible anomalies or unexpected events by inspecting how much the actual frequencies deviate from the predicted ones. The TFT technique is used to detect the effects of the Viareggio earthquake that occurred on 6 February 2022, and the structural damage induced by three simulated damage scenarios.Source: Social Science Research Network (2024). doi:10.2139/ssrn.4679906
DOI: 10.2139/ssrn.4679906
Metrics:
Falchi F., Girardi M., Gurioli G., Messina N., Padovani C., Pellegrini D.
Detecting anomalies in the vibrational features of age-old buildings is crucial within the Structural Health Monitoring (SHM) framework. The SHM techniques can leverage information from onsite measurements and environmental sources to identify the dynamic properties (such as the frequencies) of the monitored structure, searching for possible deviations or unusual behavior over time. In this paper, the Temporal Fusion Transformer (TFT) network, a deep learning algorithm initially designed for multi-horizon time series forecasting and tested on electricity, traffic, retail, and volatility problems, is applied to SHM. The TFT approach is adopted to investigate the behavior of the Guinigi Tower located in Lucca (Italy) and subjected to a long-term dynamic monitoring campaign. The TFT network is trained on the tower's experimental frequencies enriched with other environmental parameters. The transformer is then employed to predict the vibrational features (natural frequencies, root mean squares values of the velocity time series) and detect possible anomalies or unexpected events by inspecting how much the actual frequencies deviate from the predicted ones. The TFT technique is used to detect the effects of the Viareggio earthquake that occurred on 6 February 2022, and the structural damage induced by three simulated damage scenarios.Source: Social Science Research Network (2024). doi:10.2139/ssrn.4679906
DOI: 10.2139/ssrn.4679906
Metrics:
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ISTI Repository | papers.ssrn.com | CNR ExploRA
2024
Journal article
Open Access
Cascaded transformer-based networks for Wikipedia large-scale image-caption matching
Messina N., Coccomini D. A., Esuli A., Falchi F.
With the increasing importance of multimedia and multilingual data in online encyclopedias, novel methods are needed to fill domain gaps and automatically connect different modalities for increased accessibility. For example,Wikipedia is composed of millions of pages written in multiple languages. Images, when present, often lack textual context, thus remaining conceptually floating and harder to find and manage. In this work, we tackle the novel task of associating images from Wikipedia pages with the correct caption among a large pool of available ones written in multiple languages, as required by the image-caption matching Kaggle challenge organized by theWikimedia Foundation.Asystem able to perform this task would improve the accessibility and completeness of the underlying multi-modal knowledge graph in online encyclopedias. We propose a cascade of two models powered by the recent Transformer networks able to efficiently and effectively infer a relevance score between the query image data and the captions. We verify through extensive experiments that the proposed cascaded approach effectively handles a large pool of images and captions while maintaining bounded the overall computational complexity at inference time.With respect to other approaches in the challenge leaderboard,we can achieve remarkable improvements over the previous proposals (+8% in nDCG@5 with respect to the sixth position) with constrained resources. The code is publicly available at https://tinyurl.com/wiki-imcap.Source: Multimedia tools and applications (2024). doi:10.1007/s11042-023-17977-0
DOI: 10.1007/s11042-023-17977-0
Project(s): AI4Media
Metrics:
Messina N., Coccomini D. A., Esuli A., Falchi F.
With the increasing importance of multimedia and multilingual data in online encyclopedias, novel methods are needed to fill domain gaps and automatically connect different modalities for increased accessibility. For example,Wikipedia is composed of millions of pages written in multiple languages. Images, when present, often lack textual context, thus remaining conceptually floating and harder to find and manage. In this work, we tackle the novel task of associating images from Wikipedia pages with the correct caption among a large pool of available ones written in multiple languages, as required by the image-caption matching Kaggle challenge organized by theWikimedia Foundation.Asystem able to perform this task would improve the accessibility and completeness of the underlying multi-modal knowledge graph in online encyclopedias. We propose a cascade of two models powered by the recent Transformer networks able to efficiently and effectively infer a relevance score between the query image data and the captions. We verify through extensive experiments that the proposed cascaded approach effectively handles a large pool of images and captions while maintaining bounded the overall computational complexity at inference time.With respect to other approaches in the challenge leaderboard,we can achieve remarkable improvements over the previous proposals (+8% in nDCG@5 with respect to the sixth position) with constrained resources. The code is publicly available at https://tinyurl.com/wiki-imcap.Source: Multimedia tools and applications (2024). doi:10.1007/s11042-023-17977-0
DOI: 10.1007/s11042-023-17977-0
Project(s): AI4Media
Metrics:
See at:
link.springer.com | ISTI Repository | CNR ExploRA
2024
Journal article
Open Access
Product lines of dataflows
Lienhardt M., Ter Beek M. H., Damiani F.
Data-centric parallel programming models such as dataflows are well established to implement complex concurrent software. However, in a context of a configurable software, the dataflow used in its computation might vary with respect to the selected options: this happens in particular in fields such as Computational Fluid Dynamics (CFD), where the shape of the domain in which the fluid flows and the equations used to simulate the flow are all options configuring the dataflow to execute. In this paper, we present an approach to implement product lines of dataflows, based on Delta-Oriented Programming (DOP) and term rewriting. This approach includes several analyses to check that all dataflows of a product line can be generated. Moreover, we discuss a prototype implementation of the approach and demonstrate its feasibility in practice.Source: The Journal of systems and software 210 (2024). doi:10.1016/j.jss.2023.111928
DOI: 10.1016/j.jss.2023.111928
Metrics:
Lienhardt M., Ter Beek M. H., Damiani F.
Data-centric parallel programming models such as dataflows are well established to implement complex concurrent software. However, in a context of a configurable software, the dataflow used in its computation might vary with respect to the selected options: this happens in particular in fields such as Computational Fluid Dynamics (CFD), where the shape of the domain in which the fluid flows and the equations used to simulate the flow are all options configuring the dataflow to execute. In this paper, we present an approach to implement product lines of dataflows, based on Delta-Oriented Programming (DOP) and term rewriting. This approach includes several analyses to check that all dataflows of a product line can be generated. Moreover, we discuss a prototype implementation of the approach and demonstrate its feasibility in practice.Source: The Journal of systems and software 210 (2024). doi:10.1016/j.jss.2023.111928
DOI: 10.1016/j.jss.2023.111928
Metrics:
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ISTI Repository | ISTI Repository | www.sciencedirect.com | CNR ExploRA
2024
Journal article
Open Access
Fulgor: a fast and compact k-mer index for large-scale matching and color queries
Fan J., Khan J., Pratap Singh N., Pibiri G. E., Patro R.
The problem of sequence identification or matching--determining the subset of reference sequences from a given collection that are likely to contain a short, queried nucleotide sequence--is relevant for many important tasks in Computational Biology, such as metagenomics and pangenome analysis. Due to the complex nature of such analyses and the large scale of the reference collections a resource-efficient solution to this problem is of utmost importance. This poses the threefold challenge of representing the reference collection with a data structure that is efficient to query, has light memory usage, and scales well to large collections. To solve this problem, we describe an efficient colored de Bruijn graph index, arising as the combination of a k-mer dictionary with a compressed inverted index. The proposed index takes full advantage of the fact that unitigs in the colored compacted de Bruijn graph are monochromatic (i.e., all k-mers in a unitig have the same set of references of origin, or color). Specifically, the unitigs are kept in the dictionary in color order, thereby allowing for the encoding of the map from k-mers to their colors in as little as 1 + o(1) bits per unitig. Hence, one color per unitig is stored in the index with almost no space/time overhead. By combining this property with simple but effective compression methods for integer lists, the index achieves very small space. We implement these methods in a tool called Fulgor, and conduct an extensive experimental analysis to demonstrate the improvement of our tool over previous solutions. For example, compared to Themisto--the strongest competitor in terms of index space vs. query time trade-off--Fulgor requires significantly less space (up to 43% less space for a collection of 150,000 Salmonella enterica genomes), is at least twice as fast for color queries, and is 2-6× faster to construct.Source: Algorithms for molecular biology 19 (2024). doi:10.1186/s13015-024-00251-9
DOI: 10.1186/s13015-024-00251-9
Metrics:
Fan J., Khan J., Pratap Singh N., Pibiri G. E., Patro R.
The problem of sequence identification or matching--determining the subset of reference sequences from a given collection that are likely to contain a short, queried nucleotide sequence--is relevant for many important tasks in Computational Biology, such as metagenomics and pangenome analysis. Due to the complex nature of such analyses and the large scale of the reference collections a resource-efficient solution to this problem is of utmost importance. This poses the threefold challenge of representing the reference collection with a data structure that is efficient to query, has light memory usage, and scales well to large collections. To solve this problem, we describe an efficient colored de Bruijn graph index, arising as the combination of a k-mer dictionary with a compressed inverted index. The proposed index takes full advantage of the fact that unitigs in the colored compacted de Bruijn graph are monochromatic (i.e., all k-mers in a unitig have the same set of references of origin, or color). Specifically, the unitigs are kept in the dictionary in color order, thereby allowing for the encoding of the map from k-mers to their colors in as little as 1 + o(1) bits per unitig. Hence, one color per unitig is stored in the index with almost no space/time overhead. By combining this property with simple but effective compression methods for integer lists, the index achieves very small space. We implement these methods in a tool called Fulgor, and conduct an extensive experimental analysis to demonstrate the improvement of our tool over previous solutions. For example, compared to Themisto--the strongest competitor in terms of index space vs. query time trade-off--Fulgor requires significantly less space (up to 43% less space for a collection of 150,000 Salmonella enterica genomes), is at least twice as fast for color queries, and is 2-6× faster to construct.Source: Algorithms for molecular biology 19 (2024). doi:10.1186/s13015-024-00251-9
DOI: 10.1186/s13015-024-00251-9
Metrics:
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almob.biomedcentral.com | ISTI Repository | CNR ExploRA
2024
Journal article
Open Access
Integrative neuro-cardiovascular dynamics in response to test anxiety: a brain-heart axis study
Catrambone V., Zallocco L., Ramoretti E., Mazzoni M. R., Sebastiani L., Valenza G.
Test anxiety (TA), a recognized form of social anxiety, is the most prominent cause of anxiety among students and, if left unmanaged, can escalate to psychiatric disorders. TA profoundly impacts both central and autonomic nervous systems, presenting as a dual manifestation of cognitive and autonomic components. While limited studies have explored the physiological underpinnings of TA, none have directly investigated the intricate interplay between the CNS and ANS in this context. In this study, we introduce a non-invasive, integrated neurocardiovascular approach to comprehensively characterize the physiological responses of 27 healthy subjects subjected to test anxiety induced via a simulated exam scenario. Our experimental findings highlight that an isolated analysis of electroencephalographic and heart rate variability data fails to capture the intricate information provided by a brain-heart axis assessment, which incorporates an analysis of the dynamic interaction between the brain and heart. With respect to resting state, the simulated examination induced a decrease in the neural control onto heartbeat dynamics at all frequencies, while the studying condition induced a decrease in the ascending heart-to-brain interplay at EEG oscillations up to 12Hz. This underscores the significance of adopting a multisystem perspective in understanding the complex and especially functional directional mechanisms underlying test anxiety.Source: Physiology & behavior 276 (2024). doi:10.1016/j.physbeh.2024.114460
DOI: 10.1016/j.physbeh.2024.114460
Metrics:
Catrambone V., Zallocco L., Ramoretti E., Mazzoni M. R., Sebastiani L., Valenza G.
Test anxiety (TA), a recognized form of social anxiety, is the most prominent cause of anxiety among students and, if left unmanaged, can escalate to psychiatric disorders. TA profoundly impacts both central and autonomic nervous systems, presenting as a dual manifestation of cognitive and autonomic components. While limited studies have explored the physiological underpinnings of TA, none have directly investigated the intricate interplay between the CNS and ANS in this context. In this study, we introduce a non-invasive, integrated neurocardiovascular approach to comprehensively characterize the physiological responses of 27 healthy subjects subjected to test anxiety induced via a simulated exam scenario. Our experimental findings highlight that an isolated analysis of electroencephalographic and heart rate variability data fails to capture the intricate information provided by a brain-heart axis assessment, which incorporates an analysis of the dynamic interaction between the brain and heart. With respect to resting state, the simulated examination induced a decrease in the neural control onto heartbeat dynamics at all frequencies, while the studying condition induced a decrease in the ascending heart-to-brain interplay at EEG oscillations up to 12Hz. This underscores the significance of adopting a multisystem perspective in understanding the complex and especially functional directional mechanisms underlying test anxiety.Source: Physiology & behavior 276 (2024). doi:10.1016/j.physbeh.2024.114460
DOI: 10.1016/j.physbeh.2024.114460
Metrics:
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ISTI Repository | www.sciencedirect.com | CNR ExploRA
2024
Journal article
Open Access
Hypnotizability-related risky experience and behavior
Cruz-Sanabria F., Faraguna U., Panu C., Tommasi L., Bruno S., Bazzani A., Sebastiani L., Santarcangelo E. L.
Risk is the probability of an adverse event. The proneness to take a risk and the risk taking behavior differ among the general population. Hypnotizability is a stable psychophysiological trait expressing the individual proneness to modify perception, memory and behavior following specific suggestions also in the ordinary state of consciousness. Some hypnotizability-related neurophysiological and behavioral correlates suggest that hypnotizability level, measured by standard scales classifying individuals as low (lows), medium (mediums) and high hypnotizable (highs) subjects, can be related to risk propensity and risk-taking. To study whether hypnotizability modulates risk propensity and behavior, we recruited healthy participants, classified through the Standford Hypnotic Susceptibility scale, form A, and compared lows' (n = 33), mediums' (n = 19) and highs'(n = 15) experiential and behavioral risk perception and propensity variables through the Domain-specific risk-taking scale and the Balloon Analogue Risk Task. MANOVA results indicated that different hypnotizability levels are not associated with different risky behavior and experience, except for higher expected financial benefits from risky behavior in lows. However, hypnotizability-related risk profiles were identified through correlational analyses. In fact, highs exhibited a negative association between risk perception and propensity to risk-taking, whereas mediums and lows displayed a positive association between risk propensity and expected benefit. In conclusion, the highs' profile indicates a more automatic behavior with respect to mediums and lows.Source: Neuroscience letters (Print) 821 (2024). doi:10.1016/j.neulet.2024.137625
DOI: 10.1016/j.neulet.2024.137625
Metrics:
Cruz-Sanabria F., Faraguna U., Panu C., Tommasi L., Bruno S., Bazzani A., Sebastiani L., Santarcangelo E. L.
Risk is the probability of an adverse event. The proneness to take a risk and the risk taking behavior differ among the general population. Hypnotizability is a stable psychophysiological trait expressing the individual proneness to modify perception, memory and behavior following specific suggestions also in the ordinary state of consciousness. Some hypnotizability-related neurophysiological and behavioral correlates suggest that hypnotizability level, measured by standard scales classifying individuals as low (lows), medium (mediums) and high hypnotizable (highs) subjects, can be related to risk propensity and risk-taking. To study whether hypnotizability modulates risk propensity and behavior, we recruited healthy participants, classified through the Standford Hypnotic Susceptibility scale, form A, and compared lows' (n = 33), mediums' (n = 19) and highs'(n = 15) experiential and behavioral risk perception and propensity variables through the Domain-specific risk-taking scale and the Balloon Analogue Risk Task. MANOVA results indicated that different hypnotizability levels are not associated with different risky behavior and experience, except for higher expected financial benefits from risky behavior in lows. However, hypnotizability-related risk profiles were identified through correlational analyses. In fact, highs exhibited a negative association between risk perception and propensity to risk-taking, whereas mediums and lows displayed a positive association between risk propensity and expected benefit. In conclusion, the highs' profile indicates a more automatic behavior with respect to mediums and lows.Source: Neuroscience letters (Print) 821 (2024). doi:10.1016/j.neulet.2024.137625
DOI: 10.1016/j.neulet.2024.137625
Metrics:
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ISTI Repository | www.sciencedirect.com | CNR ExploRA
2024
Journal article
Open Access
Offsite evaluation of localization systems: criteria, systems and results from IPIN 2021-22 competitions
Potortì F., Crivello A.
Indoor positioning is a thriving research area which is slowly gaining market momentum. Its applications are mostly customised, ad hoc installations; ubiquitous applications analogous to GNSS for outdoors are not available because of the lack of generic platforms, widely accepted standards and interoperability protocols. In this context, the Indoor Positioning and Indoor Navigation (IPIN) competition is the only long-term, technically sound initiative to monitor the state of the art of real systems by measuring their performance in a realistic environment. Most competing systems are pedestrian-oriented and based on the use of smartphones, but several competing Tracks were set up, enabling comparison of an array of technologies. The two IPIN competitions described here include only off-site Tracks. In contrast with on-site Tracks where competitors bring their systems on site -- which were impossible to organise during 2021 and 2022 -- in off-site Tracks competitors download pre-recorded data from multiple sensors and process them using the EvaalAPI, a real-time, web-based emulation interface. As usual with IPIN competitions, Tracks were compliant with the EvAAL framework, ensuring consistency of the measurement procedure and reliability of results. The main contribution of this work is to show a compilation of possible indoor positioning scenarios and different indoor positioning solutions to the same problem.Source: IEEE journal of indoor and seamless positioning and navigation (2024): 1–39. doi:10.1109/JISPIN.2024.3355840
DOI: 10.1109/jispin.2024.3355840
Metrics:
Potortì F., Crivello A.
Indoor positioning is a thriving research area which is slowly gaining market momentum. Its applications are mostly customised, ad hoc installations; ubiquitous applications analogous to GNSS for outdoors are not available because of the lack of generic platforms, widely accepted standards and interoperability protocols. In this context, the Indoor Positioning and Indoor Navigation (IPIN) competition is the only long-term, technically sound initiative to monitor the state of the art of real systems by measuring their performance in a realistic environment. Most competing systems are pedestrian-oriented and based on the use of smartphones, but several competing Tracks were set up, enabling comparison of an array of technologies. The two IPIN competitions described here include only off-site Tracks. In contrast with on-site Tracks where competitors bring their systems on site -- which were impossible to organise during 2021 and 2022 -- in off-site Tracks competitors download pre-recorded data from multiple sensors and process them using the EvaalAPI, a real-time, web-based emulation interface. As usual with IPIN competitions, Tracks were compliant with the EvAAL framework, ensuring consistency of the measurement procedure and reliability of results. The main contribution of this work is to show a compilation of possible indoor positioning scenarios and different indoor positioning solutions to the same problem.Source: IEEE journal of indoor and seamless positioning and navigation (2024): 1–39. doi:10.1109/JISPIN.2024.3355840
DOI: 10.1109/jispin.2024.3355840
Metrics:
See at:
ieeexplore.ieee.org | CNR ExploRA
2023
Conference article
Open Access
Self-assess momentary mood in mobile devices: a case study with mature female participants
Senette C., Buzzi M. C., Paratore M. T.
Starting from the assumption that mood has a central role in domain-specific persuasion systems for well-being, the main goal of this study was to investigate the feasibility and acceptability of single-input methods to assess momentary mood as a medium for further interventions in health-related mobile apps destined for mature women. To this aim, we designed a very simple android App providing four user interfaces, each one showing one interactive widget to self-assess mood. Two widgets report a hint about the momentary mood they represent; the last two do not have the hints but were previously refined through questionnaires administered to 63 women (age 45-65) in order to reduce their expressive ambiguity. Next, fifteen women (age 45-65 years) were recruited to use the app for 15 days. Participants were polled about their mood four times a day and data were saved in a remote database. Moreover, users were asked to fill out a preliminary questionnaire, at the first access to the app, and a feedback questionnaire at the end of the testing period. Results appear to prove the feasibility and acceptability of this approach to self-assess momentary mood in the target population and provides some potential input methods to be used in this context.Source: ICCT 2023, Jaipur, India, 9-12/10/2023
Senette C., Buzzi M. C., Paratore M. T.
Starting from the assumption that mood has a central role in domain-specific persuasion systems for well-being, the main goal of this study was to investigate the feasibility and acceptability of single-input methods to assess momentary mood as a medium for further interventions in health-related mobile apps destined for mature women. To this aim, we designed a very simple android App providing four user interfaces, each one showing one interactive widget to self-assess mood. Two widgets report a hint about the momentary mood they represent; the last two do not have the hints but were previously refined through questionnaires administered to 63 women (age 45-65) in order to reduce their expressive ambiguity. Next, fifteen women (age 45-65 years) were recruited to use the app for 15 days. Participants were polled about their mood four times a day and data were saved in a remote database. Moreover, users were asked to fill out a preliminary questionnaire, at the first access to the app, and a feedback questionnaire at the end of the testing period. Results appear to prove the feasibility and acceptability of this approach to self-assess momentary mood in the target population and provides some potential input methods to be used in this context.Source: ICCT 2023, Jaipur, India, 9-12/10/2023
See at:
ISTI Repository | CNR ExploRA
2023
Journal article
Open Access
Efficient adaptive ensembling for image classification
Bruno A., Moroni D., Martinelli M.
In recent times, except for sporadic cases, the trend in Computer Vision is to achieve minor improvements over considerable increases in complexity. To reverse this tendency, we propose a novel method to boost image classification performances without an increase in complexity. To this end, we revisited ensembling, a powerful approach, not often adequately used due to its nature of increased complexity and training time, making it viable by specific design choices. First, we trained end-to-end two EfficientNet-b0 models (known to be the architecture with the best overall accuracy/complexity trade-off in image classification) on disjoint subsets of data (i.e. bagging). Then, we made an efficient adaptive ensemble by performing fine-tuning of a trainable combination layer. In this way, we were able to outperform the state-of-the-art by an average of 0.5\% on the accuracy with restrained complexity both in terms of number of parameters (by 5-60 times), and FLoating point Operations Per Second (by 10-100 times) on several major benchmark datasets, fully embracing the green AI.Source: Expert systems (Online) (2023). doi:10.1111/exsy.13424
DOI: 10.1111/exsy.13424
Metrics:
Bruno A., Moroni D., Martinelli M.
In recent times, except for sporadic cases, the trend in Computer Vision is to achieve minor improvements over considerable increases in complexity. To reverse this tendency, we propose a novel method to boost image classification performances without an increase in complexity. To this end, we revisited ensembling, a powerful approach, not often adequately used due to its nature of increased complexity and training time, making it viable by specific design choices. First, we trained end-to-end two EfficientNet-b0 models (known to be the architecture with the best overall accuracy/complexity trade-off in image classification) on disjoint subsets of data (i.e. bagging). Then, we made an efficient adaptive ensemble by performing fine-tuning of a trainable combination layer. In this way, we were able to outperform the state-of-the-art by an average of 0.5\% on the accuracy with restrained complexity both in terms of number of parameters (by 5-60 times), and FLoating point Operations Per Second (by 10-100 times) on several major benchmark datasets, fully embracing the green AI.Source: Expert systems (Online) (2023). doi:10.1111/exsy.13424
DOI: 10.1111/exsy.13424
Metrics:
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onlinelibrary.wiley.com | ISTI Repository | CNR ExploRA
2023
Conference article
Open Access
Revisiting ensembling for improving the performance of deep learning models
Bruno A., Moroni D., Martinelli M.
Ensembling is a very well-known strategy consisting in fusing several different models to achieve a new model for classification or regression tasks. Over the years, ensembling has been proven to provide superior performance in various contexts related to pattern recognition and artificial intelligence. Moreover, the basic ideas that are at the basis of ensembling have been a source of inspiration for the design of the most recent deep learning architectures. Indeed, a close analysis of those architectures shows that some connections among layers and groups of layers achieve effects similar to those obtainable by bagging, boosting and stacking, which are the well-known three basic approaches to ensembling. However, we argue that research has not fully leveraged the potential offered by ensembling. Indeed, this paper investigates some possible approaches to the combination of weak learners, or sub-components of weak learners, in the context of bagging. Based on previous results obtained in specific domains, we extend the approach to a reference dataset obtaining encouraging results.Source: ICPR 2022 - International Conference on Pattern Recognition, pp. 445–452, Montreal, Canada, 21-25/08/2022
DOI: 10.1007/978-3-031-37742-6_34
Metrics:
Bruno A., Moroni D., Martinelli M.
Ensembling is a very well-known strategy consisting in fusing several different models to achieve a new model for classification or regression tasks. Over the years, ensembling has been proven to provide superior performance in various contexts related to pattern recognition and artificial intelligence. Moreover, the basic ideas that are at the basis of ensembling have been a source of inspiration for the design of the most recent deep learning architectures. Indeed, a close analysis of those architectures shows that some connections among layers and groups of layers achieve effects similar to those obtainable by bagging, boosting and stacking, which are the well-known three basic approaches to ensembling. However, we argue that research has not fully leveraged the potential offered by ensembling. Indeed, this paper investigates some possible approaches to the combination of weak learners, or sub-components of weak learners, in the context of bagging. Based on previous results obtained in specific domains, we extend the approach to a reference dataset obtaining encouraging results.Source: ICPR 2022 - International Conference on Pattern Recognition, pp. 445–452, Montreal, Canada, 21-25/08/2022
DOI: 10.1007/978-3-031-37742-6_34
Metrics:
See at:
ISTI Repository | link.springer.com | CNR ExploRA
2023
Contribution to book
Open Access
Mesoscale events classification in sea surface temperature imagery
Reggiannini M., Janeiro J., Martins F., Papini O., Pieri G.
Sea observation through remote sensing technologies plays an essential role in understanding the health status of marine fauna species and their future behaviour. Accurate knowledge of the marine habitat and the factors affecting faunal variations allows to perform predictions and adopt proper decisions. This is even more relevant nowadays, with policymakers needing increased environmental awareness, aiming to implement sustainable policies. There is a connection between the biogeochemical and physical processes taking place within a biological system and the variations observed in its faunal populations. Mesoscale phenomena, such as upwelling, countercurrents and filaments, are essential processes to analyse because their arousal entails, among other things, variations in the density of nutrient substances, in turn affecting the biological parameters of the habitat. This paper concerns the proposal of a classification system devoted to recognising marine mesoscale events. These phenomena are studied and monitored by analysing Sea Surface Temperature images captured by satellite missions, such as Metop and MODIS Terra/Aqua. Classification of such images is pursued through dedicated algorithms that extract temporal and spatial features from the data and apply a set of rules to the extracted features, in order to discriminate between different observed scenarios. The results presented in this work have been obtained by applying the proposed approach to images captured over the south-western region of the Iberian Peninsula.Source: Machine Learning, Optimization, and Data Science, edited by Nicosia G., Ojha V., La Malfa E., La Malfa G., Pardalos P., Di Fatta G., Giuffrida G., Umeton R., pp. 516–527, 2023
DOI: 10.1007/978-3-031-25599-1_38
Project(s): NAUTILOS
Metrics:
Reggiannini M., Janeiro J., Martins F., Papini O., Pieri G.
Sea observation through remote sensing technologies plays an essential role in understanding the health status of marine fauna species and their future behaviour. Accurate knowledge of the marine habitat and the factors affecting faunal variations allows to perform predictions and adopt proper decisions. This is even more relevant nowadays, with policymakers needing increased environmental awareness, aiming to implement sustainable policies. There is a connection between the biogeochemical and physical processes taking place within a biological system and the variations observed in its faunal populations. Mesoscale phenomena, such as upwelling, countercurrents and filaments, are essential processes to analyse because their arousal entails, among other things, variations in the density of nutrient substances, in turn affecting the biological parameters of the habitat. This paper concerns the proposal of a classification system devoted to recognising marine mesoscale events. These phenomena are studied and monitored by analysing Sea Surface Temperature images captured by satellite missions, such as Metop and MODIS Terra/Aqua. Classification of such images is pursued through dedicated algorithms that extract temporal and spatial features from the data and apply a set of rules to the extracted features, in order to discriminate between different observed scenarios. The results presented in this work have been obtained by applying the proposed approach to images captured over the south-western region of the Iberian Peninsula.Source: Machine Learning, Optimization, and Data Science, edited by Nicosia G., Ojha V., La Malfa E., La Malfa G., Pardalos P., Di Fatta G., Giuffrida G., Umeton R., pp. 516–527, 2023
DOI: 10.1007/978-3-031-25599-1_38
Project(s): NAUTILOS
Metrics:
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ISTI Repository | link.springer.com | CNR ExploRA
2023
Conference article
Open Access
On the applicability of prototypical part learning in medical images: breast masses classification using ProtoPNet
Carloni G., Berti A., Iacconi C., Pascali M. A., Colantonio S.
Deep learning models have become state-of-the-art in many areas, ranging from computer vision to agriculture research. However, concerns have been raised with respect to the transparency of their decisions, especially in the image domain. In this regard, Explainable Artificial Intelligence has been gaining popularity in recent years. The ProtoPNet model, which breaks down an image into prototypes and uses evidence gathered from the prototypes to classify an image, represents an appealing approach. Still, questions regarding its effectiveness arise when the application domain changes from real-world natural images to gray-scale medical images. This work explores the applicability of prototypical part learning in medical imaging by experimenting with ProtoPNet on a breast masses classification task. The two considered aspects were the classification capabilities and the validity of explanations. We looked for the optimal model's hyperparameter configuration via a random search. We trained the model in a five-fold CV supervised framework, with mammogram images cropped around the lesions and ground-truth labels of benign/malignant masses. Then, we compared the performance metrics of ProtoPNet to that of the corresponding base architecture, which was ResNet18, trained under the same framework. In addition, an experienced radiologist provided a clinical viewpoint on the quality of the learned prototypes, the patch activations, and the global explanations. We achieved a Recall of 0.769 and an area under the receiver operating characteristic curve of 0.719 in our experiments. Even though our findings are non-optimal for entering the clinical practice yet, the radiologist found ProtoPNet's explanations very intuitive, reporting a high level of satisfaction. Therefore, we believe that prototypical part learning offers a reasonable and promising trade-off between classification performance and the quality of the related explanation.Source: ICPR 2022 - International Conference on Pattern Recognition - ICPR 2022 International Workshops and Challenges, pp. 539–557, Montreal, Canada, 21-25/08/2022
DOI: 10.1007/978-3-031-37660-3_38
Metrics:
Carloni G., Berti A., Iacconi C., Pascali M. A., Colantonio S.
Deep learning models have become state-of-the-art in many areas, ranging from computer vision to agriculture research. However, concerns have been raised with respect to the transparency of their decisions, especially in the image domain. In this regard, Explainable Artificial Intelligence has been gaining popularity in recent years. The ProtoPNet model, which breaks down an image into prototypes and uses evidence gathered from the prototypes to classify an image, represents an appealing approach. Still, questions regarding its effectiveness arise when the application domain changes from real-world natural images to gray-scale medical images. This work explores the applicability of prototypical part learning in medical imaging by experimenting with ProtoPNet on a breast masses classification task. The two considered aspects were the classification capabilities and the validity of explanations. We looked for the optimal model's hyperparameter configuration via a random search. We trained the model in a five-fold CV supervised framework, with mammogram images cropped around the lesions and ground-truth labels of benign/malignant masses. Then, we compared the performance metrics of ProtoPNet to that of the corresponding base architecture, which was ResNet18, trained under the same framework. In addition, an experienced radiologist provided a clinical viewpoint on the quality of the learned prototypes, the patch activations, and the global explanations. We achieved a Recall of 0.769 and an area under the receiver operating characteristic curve of 0.719 in our experiments. Even though our findings are non-optimal for entering the clinical practice yet, the radiologist found ProtoPNet's explanations very intuitive, reporting a high level of satisfaction. Therefore, we believe that prototypical part learning offers a reasonable and promising trade-off between classification performance and the quality of the related explanation.Source: ICPR 2022 - International Conference on Pattern Recognition - ICPR 2022 International Workshops and Challenges, pp. 539–557, Montreal, Canada, 21-25/08/2022
DOI: 10.1007/978-3-031-37660-3_38
Metrics:
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ISTI Repository | link.springer.com | CNR ExploRA
2023
Conference article
Open Access
GAM Forest explanation
Lucchese C., Orlando S., Perego R., Veneri A.
Most accurate machine learning models unfortunately produce black-box predictions, for which it is impossible to grasp the internal logic that leads to a specific decision. Unfolding the logic of such black-box models is of increasing importance, especially when they are used in sensitive decision-making processes. In thisworkwe focus on forests of decision trees, which may include hundreds to thousands of decision trees to produce accurate predictions. Such complexity raises the need of developing explanations for the predictions generated by large forests.We propose a post hoc explanation method of large forests, named GAM-based Explanation of Forests (GEF), which builds a Generalized Additive Model (GAM) able to explain, both locally and globally, the impact on the predictions of a limited set of features and feature interactions.We evaluate GEF over both synthetic and real-world datasets and show that GEF can create a GAM model with high fidelity by analyzing the given forest only and without using any further information, not even the initial training dataset.Source: EDBT 2022 - 26th International Conference on Extending Database Technology, pp. 171–182, Ioannina, Greece, 28-31/03/2023
DOI: 10.48786/edbt.2023.14
Metrics:
Lucchese C., Orlando S., Perego R., Veneri A.
Most accurate machine learning models unfortunately produce black-box predictions, for which it is impossible to grasp the internal logic that leads to a specific decision. Unfolding the logic of such black-box models is of increasing importance, especially when they are used in sensitive decision-making processes. In thisworkwe focus on forests of decision trees, which may include hundreds to thousands of decision trees to produce accurate predictions. Such complexity raises the need of developing explanations for the predictions generated by large forests.We propose a post hoc explanation method of large forests, named GAM-based Explanation of Forests (GEF), which builds a Generalized Additive Model (GAM) able to explain, both locally and globally, the impact on the predictions of a limited set of features and feature interactions.We evaluate GEF over both synthetic and real-world datasets and show that GEF can create a GAM model with high fidelity by analyzing the given forest only and without using any further information, not even the initial training dataset.Source: EDBT 2022 - 26th International Conference on Extending Database Technology, pp. 171–182, Ioannina, Greece, 28-31/03/2023
DOI: 10.48786/edbt.2023.14
Metrics:
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ISTI Repository | openproceedings.org | CNR ExploRA
2023
Contribution to book
Open Access
A case study in formal analysis of system requirements
Belli D., Mazzanti F.
One of the goals of the 4SECURail project has been to demonstrate the benefits, limits, and costs of introducing formal meth- ods in the system requirements definition process. This has been done, on an experimental basis, by applying a specific set of tools and method- ologies to a case study from the railway sector. The paper describes the approach adopted in the project and some considerations resulting from the experience.Source: Software Engineering and Formal Methods. SEFM 2022 Collocated Workshops, edited by Masci P., Bernardeschi C., Graziani P., Koddenbrock M., Palmieri M., pp. 164–173, 2023
DOI: 10.1007/978-3-031-26236-4_14
Project(s): 4SECURAIL
Metrics:
Belli D., Mazzanti F.
One of the goals of the 4SECURail project has been to demonstrate the benefits, limits, and costs of introducing formal meth- ods in the system requirements definition process. This has been done, on an experimental basis, by applying a specific set of tools and method- ologies to a case study from the railway sector. The paper describes the approach adopted in the project and some considerations resulting from the experience.Source: Software Engineering and Formal Methods. SEFM 2022 Collocated Workshops, edited by Masci P., Bernardeschi C., Graziani P., Koddenbrock M., Palmieri M., pp. 164–173, 2023
DOI: 10.1007/978-3-031-26236-4_14
Project(s): 4SECURAIL
Metrics:
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ISTI Repository | doi.org | link.springer.com | CNR ExploRA
2023
Conference article
Open Access
Automated image processing for remote sensing data classification
Reggiannini M., Papini O., Pieri G.
Remote sensing technologies allow for continuous and valuable monitoring of the Earth's various environments. In particular, coastal and ocean monitoring presents an intrinsic complexity that makes such monitoring the main source of information available. Oceans, being the largest but least observed habitat, have many different factors affecting theirs faunal variations. Enhancing the capabilities to monitor and understand the changes occurring allows us to perform predictions and adopt proper decisions. This paper proposes an automated classification tool to recognise specific marine mesoscale events. Typically, human experts monitor and analyse these events visually through remote sensing imagery, specifically addressing Sea Surface Temperature data. The extended availability of this kind of remote sensing data transforms this activity into a time-consuming and subjective interpretation of the information. For this reason, there is an increased need for automated or at least semi-automated tools to perform this task. The results presented in this work have been obtained by applying the proposed approach to images captured over the southwestern region of the Iberian Peninsula.Source: ICPR 2022 - International Workshops and Challenges, pp. 553–560, Montreal, Canada, 21-25/08/2022
DOI: 10.1007/978-3-031-37742-6_43
Project(s): NAUTILOS
Metrics:
Reggiannini M., Papini O., Pieri G.
Remote sensing technologies allow for continuous and valuable monitoring of the Earth's various environments. In particular, coastal and ocean monitoring presents an intrinsic complexity that makes such monitoring the main source of information available. Oceans, being the largest but least observed habitat, have many different factors affecting theirs faunal variations. Enhancing the capabilities to monitor and understand the changes occurring allows us to perform predictions and adopt proper decisions. This paper proposes an automated classification tool to recognise specific marine mesoscale events. Typically, human experts monitor and analyse these events visually through remote sensing imagery, specifically addressing Sea Surface Temperature data. The extended availability of this kind of remote sensing data transforms this activity into a time-consuming and subjective interpretation of the information. For this reason, there is an increased need for automated or at least semi-automated tools to perform this task. The results presented in this work have been obtained by applying the proposed approach to images captured over the southwestern region of the Iberian Peninsula.Source: ICPR 2022 - International Workshops and Challenges, pp. 553–560, Montreal, Canada, 21-25/08/2022
DOI: 10.1007/978-3-031-37742-6_43
Project(s): NAUTILOS
Metrics:
See at:
ISTI Repository | link.springer.com | CNR ExploRA