Papers by giacomo pappalardo
ASME 2006 Fourth International Conference on Fuel Cell Science, Engineering and Technology, Parts A and B, 2006
The aim of the work is to perform a comparative study of the compression system for a polymer ele... more The aim of the work is to perform a comparative study of the compression system for a polymer electrolyte membrane (PEM) fuel cell. This study wishes also to address some key-features on the suitability of different compressors with respect to the main system design issues (eg energy balance, system performance and control). According to the technologies currently considered as being able to meet fuel cell system requirements, sliding vane, twin-screw and centrifugal compressors have been studied. The analysis has been performed ...
Terapia Familiare Rivista Interdisciplinare Di Ricerca E Intervento Relazionale, 2012
International Journal of Data Science and Analytics, 2016
2014 International Conference on Data Science and Advanced Analytics, Oct 30, 2014
The large availability of mobility data allows us to investigate complex phenomena about human mo... more The large availability of mobility data allows us to investigate complex phenomena about human movement. However this adundance of data comes with few information about the purpose of movement. In this work we address the issue of activity recognition by introducing Activity-Based Cascading (ABC) classification. Such approach departs completely from probabilistic approaches for two main reasons. First, it exploits a set of structural features extracted from the Individual Mobility Network (IMN), a model able to capture the salient aspects of individual mobility. Second, it uses a cascading classification as a way to tackle the highly skewed frequency of activity classes. We show that our approach outperforms existing state-of-theart probabilistic methods. Since it reaches high precision, ABC classification represents a very reliable semantic amplifier for Big Data.
Procedia Computer Science, 2016
Studies in Computational Intelligence, 2016
2015 IEEE International Conference on Big Data (Big Data), 2015
Big Data offer nowadays the potential capability of creating a digital nervous system of our soci... more Big Data offer nowadays the potential capability of creating a digital nervous system of our society, enabling the measurement, monitoring and prediction of relevant aspects of socio-economic phenomena in quasi real time. This potential has fueled, in the last few years, a growing interest around the usage of Big Data to support official statistics in the measurement of individual and collective economic well-being. In this work we study the relations between human mobility patterns and socio-economic development. Starting from nation-wide mobile phone data we extract a measure of mobility volume and a measure of mobility diversity for each individual. We then aggregate the mobility measures at municipality level and investigate the correlations with external socio-economic indicators independently surveyed by an official statistics institute. We find three main results. First, aggregated human mobility patterns are correlated with these socio-economic indicators. Second, the diversity of mobility, defined in terms of entropy of the individual users' trajectories, exhibits the strongest correlation with the external socio-economic indicators. Third, the volume of mobility and the diversity of mobility show opposite correlations with the socio-economic indicators. Our results, validated against a null model, open an interesting perspective to study human behavior through Big Data by means of new statistical indicators that quantify and possibly "nowcast" the socio-economic development of our society.
One classic problem definition in social network analysis is the study of diffusion in networks, ... more One classic problem definition in social network analysis is the study of diffusion in networks, which enables us to tackle problems like favoring the adoption of positive technologies. Most of the attention has been turned to how to maximize the number of influenced nodes, but this approach misses the fact that different scenarios imply different diffusion dynamics, only slightly related to maximizing the number of nodes involved. In this paper we measure three different dimensions of social prominence: the Width, i.e. the ratio of neighbors influenced by a node; the Depth, i.e. the degrees of separation from a node to the nodes perceiving its prominence; and the Strength, i.e. the intensity of the prominence of a node. By defining a procedure to extract prominent users in complex networks, we detect associations between the three dimensions of social prominence and classical network statistics. We validate our results on a social network extracted from the Last.Fm music platform.
2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2015
Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015 - ASONAM '15, 2015
Traditional approaches to user engagement analysis focus on individual users. In this paper we ad... more Traditional approaches to user engagement analysis focus on individual users. In this paper we address user engagement analysis at the level of groups of users (social communities). From the entire Skype social network we extract communities by means of representative community detection methods each one providing node partitions having their own peculiarities. We then examine user engagement in the extracted communities putting into evidence clear relations between topological and geographic features of communities and their mean user engagement. In particular we show that user engagement can be to a great extent predicted from such features. Moreover, from the analysis it clearly emerges that the choice of community definition and granularity deeply affect the predictive performance.
Nature Communications, 2015
The availability of massive digital traces of human whereabouts has offered a series of novel ins... more The availability of massive digital traces of human whereabouts has offered a series of novel insights on the quantitative patterns characterizing human mobility. In particular, numerous recent studies have lead to an unexpected consensus: the considerable variability in the characteristic travelled distance of individuals coexists with a high degree of predictability of their future locations. Here we shed light on this surprising coexistence by systematically investigating the impact of recurrent mobility on the characteristic distance travelled by individuals. Using both mobile phone and GPS data, we discover the existence of two distinct classes of individuals: returners and explorers. As existing models of human mobility cannot explain the existence of these two classes, we develop more realistic models able to capture the empirical findings. Finally, we show that returners and explorers play a distinct quantifiable role in spreading phenomena and that a correlation exists between their mobility patterns and social interactions.
Journal of Official Statistics, 2015
The timely, accurate monitoring of social indicators, such as poverty or inequality, at a fine gr... more The timely, accurate monitoring of social indicators, such as poverty or inequality, at a fine grained spatial and temporal scale is a challenging task for official statistics, albeit a crucial tool for understanding social phenomena and policy making. We advocate in this paper that an interdisciplinary approach, combining the body of statistical research in small area estimates with the body of research in social data mining based on big data, can provide novel means to tackle this problem successfully. Big data sensed from the digital breadcrumbs that humans leave behind in their daily activities mediated by the ICT's are in fact providing ever more accurate proxies of social life. Social data mining from these data, coupled with advanced model-based techniques for fine-grained estimates, have the potential of providing us with a novel microscope for understanding social complexity.
2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2012
The advent of social media have allowed us to build massive networks of weak ties: acquaintances ... more The advent of social media have allowed us to build massive networks of weak ties: acquaintances and nonintimate ties we use all the time to spread information and thoughts. Conversely, strong ties are the people we really trust, people whose social circles tightly overlap with our own and, often, they are also the people most like us. Unfortunately, the social media do not incorporate tie strength in the creation and management of relationships, and treat all users the same: friend or stranger, with little or nothing in between. In the current work, we address the challenging issue of detecting on online social networks the strong and intimate ties from the huge mass of such mere social contacts. In order to do so, we propose a novel multidimensional definition of tie strength which exploits the existence of multiple online social links between two individuals. We test our definition on a multidimensional network constructed over users in Foursquare, Twitter and Facebook, analyzing the structural role of strong e weak links, and the correlations with the most common similarity measures.
2013 IEEE 13th International Conference on Data Mining Workshops, 2013
The recent emergence of the so called online social fitness constitutes a good proxy to study the... more The recent emergence of the so called online social fitness constitutes a good proxy to study the patterns underlying success in sport. Through these platforms, users can collect, monitor and share with friends their sport performance, diet, and even burned calories, giving an unprecedented opportunity to answer very fascinating questions: What are the main factors that shape sport performance? What are the characteristics that distinguish successful sportsmen? Can we characterize the role of social influence on fitness behavior? In the current work, we present the results of a study conducted on a sample of 29, 284 cyclists downloaded via APIs from the social fitness platform Strava.com. We defined two basic metrics: a measure of training effort, that is how much a cyclist struggled during the workout; and a measure of training performance indicating the results achieved during the training. Analyzing the relationship between these two metrics, an interesting result immediately emerges: at a global level, there is no correlation between effort and performance. This means that, in general, the performance is not simply a function of training: two athletes with the same level of training have different performance. However, by deeply investigating workouts time evolution and cyclists' training characteristics, we found that athletes that better improve their performance follow precise training patterns usually referred as overcompensation theory, with alternation of stress peaks and rest periods. Studies and experiments related to such theory, up to now, have always been conducted by sports doctors on a few dozen professionals athletes. To the best of our knowledge, our study is the first corroboration on large scale of this theory, mainly confirming that "engine matters", but tuning is fundamental.
2014 International Conference on Data Science and Advanced Analytics (DSAA), 2014
The large availability of mobility data allows us to investigate complex phenomena about human mo... more The large availability of mobility data allows us to investigate complex phenomena about human movement. However this adundance of data comes with few information about the purpose of movement. In this work we address the issue of activity recognition by introducing Activity-Based Cascading (ABC) classification. Such approach departs completely from probabilistic approaches for two main reasons. First, it exploits a set of structural features extracted from the Individual Mobility Network (IMN), a model able to capture the salient aspects of individual mobility. Second, it uses a cascading classification as a way to tackle the highly skewed frequency of activity classes. We show that our approach outperforms existing state-of-theart probabilistic methods. Since it reaches high precision, ABC classification represents a very reliable semantic amplifier for Big Data.
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Papers by giacomo pappalardo