Computing on evolving social networks

Over the past decade, participation in social networking services has seen an exponential growth, so that nowadays most individuals are “virtually” connected to others anywhere in the world. Consistently, analysis of human social behavior has gained momentum in the computer science research community. Several well-known phenomena in the social sciences have been revisited in a computer science perspective, with a new focus on phenomena of emerging behavior, information diffusion, opinion formation and collective intelligence. Furthermore, the recent past has witnessed a growing interest in the dynamics of these phenomena and that of the underlying social structures. This thesis investigates a number of aspects related to the study of evolving social networks and the collective phenomena they mediate. We have mainly pursued three research directions. The first line of research is in a sense functional to the other two and concerns the collection of data tracking the evolution of human interactions in the physical space and the extraction of (time) evolving networks describing these interactions. A number of available datasets describing different kinds of social networks are available on line, but few involve physical proximity of humans in real life scenarios. During our research activity, we have deployed several social experiments tracking face-to-face human interactions in the physical space. The collected datasets have been used to analyze network properties and to investigate social phenomena, as further described below. A second line of research investigates the impact of dynamics on the analytical tools used to extract knowledge from social networks. This is clearly a vast area in which research in many cases is in its early stages. We have focused on centrality, a fundamental notion in the analysis and characterization of social network structure and key to a number of Web applications and services. While many social networks of interest (resulting from “virtual” or “physical” activity) are highly dynamic, many Web information retrieval algorithms were originally designed with static networks in mind. In this thesis, we design and analyze decentralized algorithms for computing and maintaining centrality scores over time evolving networks. These algorithms refer to notions of centrality which are explicitly conceived for evolving settings and which are consistent with PageRank in important cases. A further line of research investigates the wisdom of crowds effect, an important, yet not completely understood phenomenon of collective intelligence, whereby a group typically exhibits higher predictive accuracy than its single members and often experts. Phenomena of collective intelligence involve exchange and processing of information among individuals sharing some common social structure. In many cases of interest, this structure is suitably described by an evolving social network. Studying the interplay between the evolution of the underlying social structure and the computational properties of the resulting process is an interesting and challenging task. We have focused on the quantitative analysis of this aspect, in particular the effect of the network on the accuracy of prediction. To provide a mathematical characterization, we have revisited and modified a number of models of opinion formation and diffusion originally proposed in the social sciences. Experimental analysis using data collected from some of the social experiments we conducted allowed to test soundness of the proposed models. While many of these models seem to capture important aspects of the process of opinion formation in (physical) social networks, one variant we propose achieves higher predictive accuracy and is also robust to the presence of outliers

Capturing interactions in face-to-face social networks

Online social networks, formed by cyber interactions between users, are nowadays explored in a number of papers. In this work, we present our experimental activity on Face-To-Face (F2F) social networks tracing physical interactions of humans in real-world scenarios. We briefly present the technologies to observe F2F social networks focusing on the SocioPatterns platform that we have employed in our real-world experiments. Motivated by the requirements of heterogeneous applications, we discuss how to tune the SocioPatterns collection protocol parameters in order to better capture fast interactions between users; carried out experiments confirm the effectiveness of such tuning

First Experiences with the Implementation and Evaluation of Population Protocols on Physical Devices

In this paper we present an implementation of Population Protocols, a well-known fully decentralized computational model, on physical devices. We implemented the protocols sensing platforms developed with the support of the SocioPatterns research collaboration. This implementation enabled us to evaluate the protocols on a small-scale social network and thus to demonstrate their feasibility to run on real hardware platforms. To the best of our knowledge, this is the first time Population Protocols are implemented and tested on real physical devices. We also collected traces of social interactions to setup simulations on NetLogo, and thus enable the comparison of results obtained through simulation with those obtained in real experiments

Population protocols on real social networks

In this paper we present an experimental analysis to assess the performance of Population Protocols, a well-known fully decentralized computational model, on a real, evolving social network. © 2012 Authors

Population protocols on real social networks

In this paper we present an experimental analysis to assess the performance of Population Protocols, a well-known fully decentralized computational model, on real, dynamic social networks. We set-up two infrastructures to collect data coming from wireless active RFID tags, and we asked - 120 volunteers to wear them while normally moving and interacting in the monitored areas. We then used the collected data to evaluate Population Protocols on these real topologies using NetLogo simulator. To the best of our knowledge, this is the first time population protocols are evaluated on real social networks. Copyright 2012 ACM

Distributed Sensor Network for Multi-robot Surveillance

Monitoring of populated indoor environments is crucial for the surveillance of public spaces like airports or embassies, where the behavior of people may be relevant in order to determine abnormal situations. In this paper, a surveillance system based on an integration of interactive and non-interactive heterogeneous sensors is described. As a difference with respect to traditional, pure vision-based systems, the proposed approach relies on Radio Frequency Identification (RFID) tags carried by people, multiple mobile robots (each one equipped with a laser range finder and an RFID reader), and fixed RGBD cameras. The main task of the system is to assess the presence and the position of people in the environment. This is obtained by suitably integrating data coming from heterogeneous sensors, including those mounted on board of mobile robots that are in charge of patrolling the environment. The robots also adapt their behavior according to the current situation, on the basis of a Prey-Predator scheme. Experimental results carried out both on real and on simulated data show the effectiveness of the approach. © 2014 Published by Elsevier B.V

Extending TETRA with wireless sensor networks

The Terrestrial Trunked Radio (TETRA) system is an open standard developed by ETSI and designed to support mobile radio communications in a number of market segments, among which public safety is by far the largest one. In this paper we present the activities ot the "TETRis - TETRA Innovative Open Source Services" (TETRis) project to integrate Wireless Sensor Networks technology with TETRA, in order to support real-time feedback in two application contexts relevant in public safety scenarios: structural health monitoring and air quality monitoring. The WSN deployed in our testbed is based on the MagoNode platform, a new mote featuring an RF front-end capable to enhance the radio performance. The results of the experimental activity confirm that WSNs can be effectively used to support the management of critical situations in the considered scenarios and that the MagoNode platform well meet the requirements provided by experts on structural health monitoring

Multi-robot Surveillance Through a Distributed Sensor Network

Automatic surveillance of public areas, such as airports, train stations, and shopping malls, requires the capacity of detecting and recognizing possible abnormal situations in populated environments. In this book chapter, an architecture for intelligent surveillance in indoor public spaces, based on an integration of interactive and non-interactive heterogeneous sensors, is described. As a difference with respect to traditional, passive and pure vision-based systems, the proposed approach relies on a distributed sensor network combining RFID tags, multiple mobile robots, and fixed RGBD cameras. The presence and the position of people in the scene is detected by suitably combining data coming from the sensor nodes, including those mounted on board of the mobile robots that are in charge of patrolling the environment. The robots can adapt their behavior according to the current situation, on the basis of a Prey-Predator scheme, and can coordinate their actions to fulfill the required tasks. Experimental results have been carried out both on real and on simulated data to show the effectiveness of the proposed approach

ProvinciaSense: Extending the Capillary WiFi Infrastructure of Lazio Region with Static and Mobile Sensor Networks

ProvinciaSense is a proof-of-concept of the integration of both static and mobile sensor nodes with ProvinciaWiFi, a network which currently hosts more than 1100 WiFi Access Points (APs) serving Rome and other 120 cities with about 4.5 millions of citizens. The simple and cost efective integration of sensor nodes with a big number of ProvinciaWiFi APs will allow us to deploy one among the biggest available testbeds for wireless sensor networks and to support the implementation of efective smart cities services

Impact of COVID-19 Pandemic on the Diagnostic and Therapeutic Management of Endometrial Cancer: A Monocentric Retrospective Comparative Study

Endometrial cancer represents an ideal target to evaluate the impact of COVID-19 being the most frequent gynecological malignancy in Italy, generally detected at early stages and correlated with favorable oncological outcomes. The present comparative retrospective study carried out at Campus Bio-medico University Foundation in Rome aims to evaluate the impact of the COVID-19 pandemic on the presentation, diagnosis and treatment of EC. All women with a histological diagnosis of non-endometrioid and endometrioid endometrial cancer between 1 March 2018 and 31 October 2022 were included. The number of cases was higher in period 2 (95 vs. 64 cases). Time to diagnosis did not show statistically significant differences but in period 2, 92.06% of the diagnoses were made following abnormal uterine bleeding, while in period 1, only 67.02% were. The waiting time for the intervention was significantly shorter in period 2. Definitive histology, FIGO staging, surgical technique and adjuvant therapy did not show significant differences between the two periods. The study demonstrates that the impact of the COVID-19 pandemic did not have a direct effect on the diagnostic delay, tumor staging and type of therapy but rather on the presentation pattern of endometrial cancer