Using Ontologies for Semantic Data Integration

While big data analytics is considered as one of the most important paths to competitive advantage of today’s enterprises, data scientists spend a comparatively large amount of time in the data preparation and data integration phase of a big data project. This shows that data integration is still a major challenge in IT applications. Over the past two decades, the idea of using semantics for data integration has become increasingly crucial, and has received much attention in the AI, database, web, and data mining communities. Here, we focus on a specific paradigm for semantic data integration, called Ontology-Based Data Access (OBDA). The goal of this paper is to provide an overview of OBDA, pointing out both the techniques that are at the basis of the paradigm, and the main challenges that remain to be addressed

MAGISTER: Multidimensional Archival Geographical Intelligent System for Territorial Enhancement and Representation

Nel 2014 la partecipazione al bando della Regione Lazio (LR 13/2008) per progetti di ricerca presentati da Università è stata vista come un’occasione per valorizzare la mission multidisciplinare del Dipartimento di Scienze documentarie, linguistico-filologiche e geografiche della Sapienza Università di Roma. Il progetto MAGISTER è portato avanti secondo tre assi principali: la geografia, l’archivistica e l’informatica umanistica, che si permeano e si contaminano vicendevolmente.The paper describes interdisciplinary approach, goals and output of MAGISTER project, whose main aim is the creation of an Ontology-Based Data Access (OBDA) in order to manage and put in relation different kind of data and sources starting from both spatial and geographical informatio

Intriguing properties of synthetic images: from generative adversarial networks to diffusion models

Detecting fake images is becoming a major goal of computer vision. This need is becoming more and more pressing with the continuous improvement of synthesis methods based on Generative Adversarial Networks (GAN), and even more with the appearance of powerful methods based on Diffusion Models (DM). Towards this end, it is important to gain insight into which image features better discriminate fake images from real ones. In this paper we report on our systematic study of a large number of image generators of different families, aimed at discovering the most forensically relevant characteristics of real and generated images. Our experiments provide a number of interesting observations and shed light on some intriguing properties of synthetic images: (1) not only the GAN models but also the DM and VQ-GAN (Vector Quantized Generative Adversarial Networks) models give rise to visible artifacts in the Fourier domain and exhibit anomalous regular patterns in the autocorrelation; (2) when the dataset used to train the model lacks sufficient variety, its biases can be transferred to the generated images; (3) synthetic and real images exhibit significant differences in the mid-high frequency signal content, observable in their radial and angular spectral power distributions

M3Dsynth: A dataset of medical 3D images with AI-generated local manipulations

The ability to detect manipulated visual content is becoming increasingly important in many application fields, given the rapid advances in image synthesis methods. Of particular concern is the possibility of modifying the content of medical images, altering the resulting diagnoses. Despite its relevance, this issue has received limited attention from the research community. One reason is the lack of large and curated datasets to use for development and benchmarking purposes. Here, we investigate this issue and propose M3Dsynth, a large dataset of manipulated Computed Tomography (CT) lung images. We create manipulated images by injecting or removing lung cancer nodules in real CT scans, using three different methods based on Generative Adversarial Networks (GAN) or Diffusion Models (DM), for a total of 8,577 manipulated samples. Experiments show that these images easily fool automated diagnostic tools. We also tested several state-of-the-art forensic detectors and demonstrated that, once trained on the proposed dataset, they are able to accurately detect and localize manipulated synthetic content, even when training and test sets are not aligned, showing good generalization ability. Dataset and code are publicly available at https://grip-unina.github.io/M3Dsynth/

Synthetic Image Verification in the Era of Generative AI: What Works and What Isn't There Yet

In this work we present an overview of approaches for the detection and attribution of synthetic images and highlight their strengths and weaknesses. We also point out and discuss hot topics in this field and outline promising directions for future research

Road traffic pollution and childhood leukemia: a nationwide case-control study in Italy

Background The association of childhood leukemia with traffic pollution was considered in a number of studies from 1989 onwards, with results not entirely consistent and little information regarding subtypes. Aim of the study We used the data of the Italian SETIL case-control on childhood leukemia to explore the risk by leukemia subtypes associated to exposure to vehicular traffic. Methods We included in the analyses 648 cases of childhood leukemia (565 Acute lymphoblastic–ALL and 80 Acute non lymphoblastic-AnLL) and 980 controls. Information on traffic exposure was collected from questionnaire interviews and from the geocoding of house addresses, for all periods of life of the children. Results We observed an increase in risk for AnLL, and at a lower extent for ALL, with indicators of exposure to traffic pollutants. In particular, the risk was associated to the report of closeness of the house to traffic lights and to the passage of trucks (OR: 1.76; 95% CI 1.03–3.01 for ALL and 6.35; 95% CI 2.59–15.6 for AnLL). The association was shown also in the analyses limited to AML and in the stratified analyses and in respect to the house in different period of life. Conclusions Results from the SETIL study provide some support to the association of traffic related exposure and risk for AnLL, but at a lesser extent for ALL. Our conclusion highlights the need for leukemia type specific analyses in future studies. Results support the need of controlling exposure from traffic pollution, even if knowledge is not complete

Landslide-Induced Damage Probability Estimation Coupling InSAR and Field Survey Data by Fragility Curves

Landslides are considered to be one of the main natural geohazards causing relevant economic damages and social effects worldwide. Italy is one of the countries worldwide most affected by landslides; in the Region of Tuscany alone, more than 100,000 phenomena are known and mapped. The possibility to recognize, investigate, and monitor these phenomena play a key role to avoid further occurrences and consequences. The number of applications of Advanced Differential Interferometric Synthetic Aperture Radar (A-DInSAR) analysis for landslides monitoring and mapping greatly increased in the last decades thanks to the technological advances and the development of advanced processing algorithms. In this work, landslide-induced damage on structures recognized and classified by field survey and velocity of displacement re-projected along the steepest slope were combined in order to extract fragility curves for the hamlets of Patigno and Coloretta, in the Zeri municipality (Tuscany, northern Italy). Images using ERS1/2, ENVISAT, COSMO-SkyMed (CSK) and Sentinel-1 SAR (Synthetic Aperture Radar) were employed to investigate an approximate 25 years of deformation affecting both hamlets. Three field surveys were conducted for recognizing, identifying, and classifying the landslide-induced damage on structures and infrastructures. At the end, the damage probability maps were designed by means of the use of the fragility curves between Sentinel-1 velocities and recorded levels of damage. The results were conceived to be useful for the local authorities and civil protection authorities to improve the land managing and, more generally, for planning mitigation strategies.This work has been carried out within the project founded and supported by the Regional government of Tuscany, under the agreement “Monitoring ground deformation in the Tuscany Region with satellite radar data”. Roberto Tomás was supported by the Spanish Ministry of Economy, Industry and Competitiveness (MINECO), the State Agency of Research (AEI) and European Funds for Regional Development (FEDER), under projects TEC2017-85244-C2-1-P and PRX17/00439

Collaborative Practices and Multidisciplinary Research : The Dialogue Between Entrepreneurship, Management, and Data Science

Author's accepted version (post-print).Available from 06/06/2020.acceptedVersio

Diagnostic accuracy of coronary computed tomography angiography for the evaluation of obstructive coronary artery disease in patients referred for transcatheter aortic valve implantation: a systematic review and meta-analysis

OBJECTIVE: To evaluate the diagnostic accuracy of coronary computed tomography angiography (CCTA) for the evaluation of obstructive coronary artery disease (CAD) in patients referred for transcatheter aortic valve implantation (TAVI). METHODS: EMBASE, PubMed/MEDLINE, and CENTRAL were searched for studies reporting accuracy of CCTA for the evaluation of obstructive CAD compared with invasive coronary angiography (ICA) as the reference standard. QUADAS-2 tool was used to assess the risk of bias. A bivariate random effects model was used to analyze, pool, and plot the diagnostic performance measurements across studies. Pooled sensitivity, specificity, positive ( + LR) and negative (−LR) likelihood ratio, diagnostic odds ratio (DOR), and hierarchical summary ROC curve (HSROC) were evaluated. Prospero registration number: CRD42021252527. RESULTS: Fourteen studies (2533 patients) were included. In the intention-to-diagnose patient-level analysis, sensitivity and specificity for CCTA were 97% (95% CI: 94–98%) and 68% (95% CI: 56–68%), respectively, and + LR and −LR were 3.0 (95% CI: 2.1–4.3) and 0.05 (95% CI: 0.03 – 0.09), with DOR equal to 60 (95% CI: 30–121). The area under the HSROC curve was 0.96 (95% CI: 0.94–0.98). No significant difference in sensitivity was found between single-heartbeat and other CT scanners (96% (95% CI: 90 – 99%) vs. 97% (95% CI: 94–98%) respectively; p = 0.37), whereas the specificity of single-heartbeat scanners was higher (82% (95% CI: 66–92%) vs. 60% (95% CI: 46 – 72%) respectively; p < 0.0001). Routine CCTA in the pre-TAVI workup could save 41% (95% CI: 34 – 47%) of ICAs if a disease prevalence of 40% is assumed. CONCLUSIONS: CCTA proved an excellent diagnostic accuracy for assessing obstructive CAD in patients referred for TAVI; the use of single-heartbeat CT scanners can further improve these findings. KEY POINTS: • CCTA proved to have an excellent diagnostic accuracy for assessing obstructive CAD in patients referred for TAVI. • Routine CCTA in the pre-TAVI workup could save more than 40% of ICAs. • Single-heartbeat CT scanners had higher specificity than others in the assessment of obstructive CAD in patients referred for TAVI. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00330-022-08603-y