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FEDERICA ROLLO

Dottorando presso: Dipartimento di Ingegneria "Enzo Ferrari"


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Pubblicazioni

2021 - Air Quality Sensor Network Data Acquisition, Cleaning, Visualization, and Analytics: A Real-world IoT Use Case [Relazione in Atti di Convegno]
Rollo, Federica; Sudharsan, Bharath; Po, Laura; Breslin, John
abstract


2021 - SenseBoard: Sensor monitoring for air quality experts [Relazione in Atti di Convegno]
Rollo, F.; Po, L.
abstract

Air quality monitoring is crucial within cities since air pollution is one of the main causes of premature death in Europe. However, performing trustworthy monitoring of urban air quality is not a simple process. Especially, if you want to try to create extensive and timely monitoring of the entire urban area using low-cost sensors. In order to collect reliable measurements from low-cost sensors, a lot of work is required from environmental experts who deploy and maintain the air quality network, and daily calibrate, control, and clean up the data generated by these sensors. In this paper, we describe SenseBoard, an interactive dashboard created to support environmental experts in the sensor network control, management of sensor data calibration, and anomaly detection.


2021 - Using Word Embeddings for Italian Crime News Categorization [Relazione in Atti di Convegno]
Bonisoli, Giovanni; Rollo, Federica; Po, Laura
abstract


2020 - Crime event localization and deduplication [Relazione in Atti di Convegno]
Rollo, Federica; Po, Laura
abstract


2020 - Interlaboratory concordance of p16/Ki-67 dual-staining interpretation in HPV-positive women in a screening population [Articolo su rivista]
Benevolo, M.; Mancuso, P.; Allia, E.; Gustinucci, D.; Bulletti, S.; Cesarini, E.; Carozzi, F. M.; Confortini, M.; Bisanzi, S.; Carlinfante, G.; Rubino, T.; Rollo, F.; Marchi, N.; Farruggio, A.; Pusiol, T.; Venturelli, F.; Giorgi Rossi, P.
abstract

Background: p16/Ki-67 dual staining is a candidate biomarker for triaging human papillomavirus (HPV)–positive women. Reproducibility is needed for adopting a test for screening. This study assessed interlaboratory reproducibility in HPV-positive women. Methods: All women positive for HPV from the Italian New Technologies for Cervical Cancer 2 study, were included in this study. ThinPrep slides were immunostained for p16/Ki-67 in 4 laboratories and were interpreted in 7 laboratories. Each slide had 3 reports from different laboratories. Slides were classified as valuable or inadequate, and valuable slides were classified as positive (at least 1 double-stained cell) or negative. Interlaboratory reproducibility was evaluated with κ values. Results: Overall, we obtained 9300 reports for 3100 cases; 905 reports (9.7%) were inadequate. The overall adequacy concordance was poor (κ = 0.224; 95% confidence interval [CI], 0.183-0.263). The overall positivity concordance was moderate (κ = 0.583; 95% CI, 0.556-0.610). Of the 176 cervical intraepithelial neoplasia 2+ (CIN-2+) lesions found in HPV DNA–positive women, 158 had a valid result: 107 were positive in all 3 reports (sensitivity for CIN-2+, 67.7%; 95% CI, 59.8%-74.9%), 23 were positive in 2 reports (sensitivity of the majority report, 82.3%; 95% CI, 75.4%-87.9%), and 15 were positive in 1 report (sensitivity of at least 1 positive result, 91.8%; 95% CI, 86.3%-95.5%). Thirteen CIN-2+ cases were negative in all 3 reports. The overall positivity concordance in CIN-2+ samples was κ = 0.487 (95% CI, 0.429-0.534), whereas in the non–CIN-2+ samples, it was κ = 0.558 (95% CI, 0.528-0.588). Conclusions: The p16/Ki-67 assay showed poor reproducibility for adequacy and good reproducibility for positivity comparable to that of cervical cytology. Nevertheless, the low reproducibility does not affect the sensitivity for CIN-2+.


2020 - Real-time data cleaning in traffic sensor networks [Relazione in Atti di Convegno]
Bachechi, Chiara; Rollo, Federica; Po, Laura
abstract


2020 - Semantic Traffic Sensor Data: The TRAFAIR Experience [Articolo su rivista]
Desimoni, Federico; Ilarri, Sergio; Po, Laura; Rollo, Federica; Trillo Lado, Raquel
abstract

Modern cities face pressing problems with transportation systems including, but not limited to, traffic congestion, safety, health, and pollution. To tackle them, public administrations have implemented roadside infrastructures such as cameras and sensors to collect data about environmental and traffic conditions. In the case of traffic sensor data not only the real-time data are essential, but also historical values need to be preserved and published. When real-time and historical data of smart cities become available, everyone can join an evidence-based debate on the city’s future evolution. The TRAFAIR (Understanding Traffic Flows to Improve Air Quality) project seeks to understand how traffic affects urban air quality. The project develops a platform to provide real-time and predicted values on air quality in several cities in Europe, encompassing tasks such as the deployment of low-cost air quality sensors, data collection and integration, modeling and prediction, the publication of open data, and the development of applications for end-users and public administrations. This paper explicitly focuses on the modeling and semantic annotation of traffic data. We present the tools and techniques used in the project and validate our strategies for data modeling and its semantic enrichment over two cities: Modena (Italy) and Zaragoza (Spain). An experimental evaluation shows that our approach to publish Linked Data is effective.


2020 - Using real sensors data to calibrate a traffic model for the city of Modena [Relazione in Atti di Convegno]
BACHECHI, CHIARA; ROLLO, FEDERICA; DESIMONI, FEDERICO; PO, Laura
abstract

In Italy, road vehicles are the preferred mean of transport. Over the last years, in almost all the EU Member States, the passenger car fleet increased. The high number of vehicles complicates urban planning and often results in traffic congestion and areas of increased air pollution. Overall, efficient traffic control is profitable in individual, societal, financial, and environmental terms. Traffic management solutions typically require the use of simulators able to capture in detail all the characteristics and dependencies associated with real-life traffic. Therefore, the realization of a traffic model can help to discover and control traffic bottlenecks in the urban context. In this paper, we analyze how to better simulate vehicle flows measured by traffic sensors in the streets. A dynamic traffic model was set up starting from traffic sensors data collected every minute in about 300 locations in the city of Modena. The reliability of the model is discussed and proved with a comparison between simulated values and real values from traffic sensors. This analysis pointed out some critical issues. Therefore, to better understand the origin of fake jams and incoherence with real data, we approached different configurations of the model as possible solutions.


2019 - From Sensors Data to Urban Traffic Flow Analysis [Relazione in Atti di Convegno]
Po, Laura; Rollo, Federica; Bachechi, Chiara; Corni, Alberto
abstract

By 2050, almost 70% of the population will live in cities. As the population grows, travel demand increases and this might affect air quality in urban areas. Traffic is among the main sources of pollution within cities. Therefore, monitoring urban traffic means not only identifying congestion and managing accidents but also preventing the impact on air pollution. Urban traffic modeling and analysis is part of the advanced traffic intelligent management technologies that has become a crucial sector for smart cities. Its main purpose is to predict congestion states of a specific urban transport network and propose improvements in the traffic network that might result into a decrease of the travel times, air pollution and fuel consumption. This paper describes the implementation of an urban traffic flow model in the city of Modena based on real traffic sensor data. This is part of a wide European project that aims at studying the correlation among traffic and air pollution, therefore at combining traffic and air pollution simulations for testing various urban scenarios and raising citizen awareness about air quality where necessary.


2019 - TRAFAIR: Understanding Traffic Flow to Improve Air Quality [Relazione in Atti di Convegno]
Po, Laura; Rollo, Federica; Ramòn Rìos Viqueira, Josè; Trillo Lado, Raquel; Bigi, Alessandro; Cacheiro Lòpez, Javier; Paolucci, Michela; Nesi, Paolo
abstract

Environmental impacts of traffic are of major concern throughout many European metropolitan areas. Air pollution causes 400 000 deaths per year, making it first environmental cause of premature death in Europe. Among the main sources of air pollution in Europe, there are road traffic, domestic heating, and industrial combustion. The TRAFAIR project brings together 9 partners from two European countries (Italy and Spain) to develop innovative and sustainable services combining air quality, weather conditions, and traffic flows data to produce new information for the benefit of citizens and government decision-makers. The project is started in November 2018 and lasts two years. It is motivated by the huge amount of deaths caused by the air pollution. Nowadays, the situation is particularly critical in some member states of Europe. In February 2017, the European Commission warned five countries, among which Spain and Italy, of continued air pollution breaches. In this context, public administrations and citizens suffer from the lack of comprehensive and fast tools to estimate the level of pollution on an urban scale resulting from varying traffic flow conditions that would allow optimizing control strategies and increase air quality awareness. The goals of the project are twofold: monitoring urban air quality by using sensors in 6 European cities and making urban air quality predictions thanks to simulation models. The project is co-financed by the European Commission under the CEF TELECOM call on Open Data.


2018 - Building an Urban Theft Map by Analyzing Newspaper Crime Reports [Relazione in Atti di Convegno]
Po, Laura; Rollo, Federica
abstract

One of the main issues in today's cities is related to public safety, which can be improved by implementing a systematic analysis for identifying and analyzing patterns and trends in crime also called crime mapping. Mapping crime allows police analysts to identify crime hot spots, moreover it increases public confidence and citizen engagement and promotes transparency.This paper is focused on analyzing and mapping thefts through on-line newspaper using text mining techniques for an Italian city.


2017 - Student research abstract: A key-entity graph for clustering multichannel news [Relazione in Atti di Convegno]
Rollo, Federica
abstract

Social networks (SN) have gained a very important role in the dissemination of news, since they allow a greater share of news than web sites and are more timely to provide updates, publishing more updated versions of the same news on the same day. The use of a variety of communication media (or channels) stimulates the need for integration and analysis of the huge amount of information published globally. The scale and heterogeneity of these messages makes the analysis of news very challenging. This paper presents an in-progress research work: the definition of a tool for clustering news according to their topics in order to understand whether there are correlations between news published by different newspapers on the same channel or by the same newspaper on different channels. We started the implementation of a method [3] based on the Keygraph algorithm [4] in order to perform multichannel clustering of news according to their topics. In this paper, we extend the proposed method [3] by considering entities in addition to the keywords to detect topics. We argue that each event can be described by entities such as times, locations, persons, things and topics. Detecting entities in a news might improve the clustering results.


2017 - Topic detection in multichannel Italian newspapers [Relazione in Atti di Convegno]
Po, Laura; Rollo, Federica; Lado, Raquel Trillo
abstract

Nowadays, any person, company or public institution uses and exploits different channels to share private or public information with other people (friends, customers, relatives, etc.) or institutions. This context has changed the journalism, thus, the major newspapers report news not just on its own web site, but also on several social media such as Twitter or YouTube. The use of multiple communication media stimulates the need for integration and analysis of the content published globally and not just at the level of a single medium. An analysis to achieve a comprehensive overview of the information that reaches the end users and how they consume the information is needed. This analysis should identify the main topics in the news flow and reveal the mechanisms of publication of news on different media (e.g. news timeline). Currently, most of the work on this area is still focused on a single medium. So, an analysis across different media (channels) should improve the result of topic detection. This paper shows the application of a graph analytical approach, called Keygraph, to a set of very heterogeneous documents such as the news published on various media. A preliminary evaluation on the news published in a 5 days period was able to identify the main topics within the publications of a single newspaper, and also within the publications of 20 newspapers on several on-line channels.