Archivio della ricerca - Fondazione Bruno Kessler
Not a member yet
21227 research outputs found
Sort by
Extensible decentralized secret sharing and application to Schnorr signatures
No full textStarting from links between Coding Theory and Secret Sharing Schemes, we develop an extensible and decentralized version of Shamir Secret Sharing, that allows the addition of new users after the initial share distribution. On top of it we design a totally decentralized (t, n)-threshold Schnorr signature scheme that needs only t users online during the key generation phase, while the others join later. Under standard assumptions we prove our scheme secure against adaptive malicious adversaries. Furthermore, we show how our security notion can be strengthened when considering a rushing adversary. Using a classical game-based argument, we prove that if there is an adversary capable of forging the scheme with non-negligible probability, then we can build a forger for the centralized Schnorr scheme with non-negligible probability
Integrated Genomic and Epidemiological Surveillance to Monitor SARS‐CoV‐2 Variants in Italy: Insights From the JN.1 Case Study (2023–2024)
Full text linkThe epidemiology of SARS-CoV-2 is marked by the continuous emergence of new lineages. Early detection and assessment of their transmissibility can be challenging for surveillance systems that rely solely on case time series data. Genomic surveillance, focusing on identifying and characterizing circulating variants, can provide early insights into their epidemiological impact. Phylogenetic and phylodynamic methods were applied to sequence data collected between October 2023 and January 2024 to study the transmission of the JN.1 variant in Italy. The genomic surveillance encompassed two data flows: flash surveys estimating variant prevalence and continuous sampling to identify emerging variants. We estimated the effective reproduction number (Re) of JN.1 using a phylodynamic birth-death model. Results were compared with the daily net reproduction number (Rt) of SARS-CoV-2 estimated from time series of hospital admissions recorded through epidemiological surveillance. We traced back the appearance of JN.1 in Italy to October 2023, with subvariants emerging and co-circulating shortly thereafter. JN.1 became dominant nationwide by the end of 2023. According to phylodynamic analysis, the Re of JN.1 was 1.73 (95% CI: 1.36-2.28) in mid-November, and its transmissibility declined over the following months. This trend aligned with Rt estimates from epidemiological surveillance, encompassing all co-circulating lineages. The high transmissibility of JN.1 anticipated the rise in its prevalence in the population and showed a temporal correlation with a transient increase in COVID-19 hospitalizations. Integrating genomic and epidemiological surveillance enhances pathogen monitoring and the assessment of new lineages' transmissibility, providing complementary evidence to patterns observed through standard surveillance
Multispectral airborne laser scanning for tree species classification: A benchmark of machine learning and deep learning algorithms
No full textClimate-smart and biodiversity-preserving forestry demands precise information on forest resources, extending to the individual tree level. Multispectral airborne laser scanning (ALS) has shown promise in automated point cloud processing, but challenges remain in leveraging deep learning techniques and identifying rare tree species in class-imbalanced datasets. This study addresses these gaps by conducting a comprehensive benchmark of deep learning and traditional shallow machine learning methods for tree species classification. For the study, we collected high-density multispectral ALS data (>1000pts∕m2) at three wavelengths using the FGI-developed HeliALS system, complemented by existing Optech Titan data (35 pts∕m2), to evaluate the species classification accuracy of various algorithms in a peri-urban study area located in southern Finland. We established a field reference dataset of 6326 segments across nine species using a newly developed browserbased crowdsourcing tool, which facilitated efficient data annotation. The ALS data, including a training dataset of 1065 segments, was shared with the scientific community to foster collaborative research and diverse algorithmic contributions. Based on 5261 test segments, our findings demonstrate that point-based deep learning methods, particularly a point transformer model, outperformed traditional machine learning and image-based deep learning approaches on high-density multispectral point clouds. For the high-density ALS dataset, a point transformer model provided the best performance reaching an overall (macro-average) accuracy of 87.9% (74.5%) with a training set of 1065 segments and 92.0% (85.1%) with a larger training set of 5000 segments. With 1065 training segments, the best image-based deep learning method, DetailView, reached an overall (macro-average) accuracy of 84.3% (63.9%), whereas a shallow random forest (RF) classifier achieved an overall (macro-average) accuracy of 83.2% (61.3%). For the sparser ALS dataset, an RF model topped the list with an overall (macro-average) accuracy of 79.9% (57.6%), closely followed by the point transformer at 79.6% (56.0%). Importantly, the overall classification accuracy of the point transformer model on the HeliALS data increased from 73.0% with no spectral information to 84.7% with single-channel reflectance, and to 87.9% with spectral information of all the three channels. Furthermore, we studied the scaling of the classification accuracy as a function of point density and training set size using 5-fold crossvalidation of our dataset. Based on our findings, multispectral information is especially beneficial for sparse point clouds with 1–50 pts∕m2. Furthermore, we observed that the classification error follows a power law ε(m) ≈ m−α as a function of the training set size m, and the classification error of the point transformer reduced significantly faster with increasing training set size compared to RF
Empowering Through Play: Leveraging Gamification for Discussing Sensitive Topics
No full textIn today’s digital age, promoting youth’s digital well-being is essential, especially when addressing sensitive issues like online grooming, cyberbullying, and gender-based violence. This paper introduces two gamified platforms: (i) StandByMe, focused on addressing gender-based violence among adolescents, and (ii) Cesagram, designed to combat online grooming and child sexual abuse in younger children. In developing the platforms, we prioritized digital well-being throughout the design process to provide a safe and supportive learning environment. This paper explores how gamification strategies were integrated to boost user engagement while safeguarding their well-being. We outline the challenges faced during the
design of these platforms and explain how we addressed them by drawing on existing literature and engaging in dialogue with subject matter experts.
Additionally, we identify novel aspects of gamifying educational content that emerged due to the sensitive nature of these topics
Assessing the Mechanism of NH3 Sensing in Flexible Carbon‐Nanotube‐Based Chemoresistive Sensors via In Situ Photoemission Spectroscopy
No full textImmersive Gamification in the Metaverse for Sustainable Development: State of the Art and Guidelines
No full textRadiation-resistant thin LGADs for enhanced 4D tracking
No full textThis contribution presents the results of an R&D study on the EXFLU1 batch, a novel concept of thin Low Gain Avalanche Diodes (LGADs) designed to achieve improved timing resolution and radiation tolerance up to 25 × 1014 ncm−2. Sensors with different thicknesses (15, 20, 30, and ) have been explored. New designs for the gain layer and guard ring structure have been investigated, along with two different doping activation methods. The sensors have been irradiated up to 25 × 1014 ncm−2 and characterised in terms of current, capacitance, and gain to assess their radiation tolerance. Finally, LGADs were tested in a 4 GeV electron beam to evaluate their timing resolution
