50 research outputs found
Where is my next friend? Recommending enjoyable profiles in location based services
No full textHow many of your friends, with whom you enjoy spending some time, live close by? How many people are at your reach, with whom you could have a nice conversation? We introduce a measure of enjoyability that may be the basis for a new class of location-based services aimed at maximizing the likelihood that two persons, or a group of people, would enjoy spending time together. Our enjoyability takes into account both topic similarity between two users and the users’ tendency to connect to people with similar or dissimilar interest. We computed the enjoyability on two datasets of geo-located tweets, and we reasoned on the applicability of the obtained results for producing friend recommendations. We aim at suggesting couples of users which are not friends yet, but which are frequently co-located and maximize our enjoyability measure. By taking into account the spatial dimension, we show how 50% of users may find at least one enjoyable person within 10km of their two most visited locations. Our results are encouraging, and open the way for a new class of recommender systems based on enjoyability
Social or Green? A Data-Driven Approach for More Enjoyable Carpooling
No full textCarpooling, i.e. the sharing of vehicles to reach common destinations, is often performed to reduce costs and pollution. Recent works on carpooling and journey planning take into account, besides mobility match, also social aspects and, more generally, non-monetary rewards. In line with this, we present a data-driven methodology for a more enjoyable carpooling. We introduce a measure of enjoyability based on people's interests, social links, and tendency to connect to people with similar or dissimilar interests. We devise a methodology to compute enjoyability from crowd-sourced data, and we show how this can be used on real world datasets to optimize for both mobility and enjoyability. Our methodology was tested on real data from Rome and San Francisco. We compare the results of an optimization model minimizing the number of cars, and a greedy approach maximing the enjoyability. We evaluate them in terms of cars saved, and average enjoyability of the system. We present also the results of a user study, with more than 200 users reporting an interest of 39% in the enjoyable solution. Moreoever, 24% of people declared that sharing the car with interesting people would be the primary motivation for carpooling
La violenza morale e psicologica sul minore nelle coppie separate: La sindrome di alienazione genitoriale PAS, due casi emblematici
No full textLa Sindrome di Alienazione Genitoriale viene descritta da Gardner, a partire dagli anni ottanta, come un disturbo psicopatologico che colpisce soggetti in età evolutiva al momento della separazione dei genitori. Una patologia relazionale osservata nelle situazioni di separazione e divorzio conflittuali e che insorge principalmente nel contesto delle controversie per l’affidamento e la custodia dei figli. La sua manifestazione principale è una forte ed ingiustificata campagna di denigrazione rivolta contro un genitore. Gardner (1985) ha individuato 12 aspetti che caratterizzano la PAS, proponendo tre livelli della sindrome: lieve, medio, grave. Verranno presentati due casi di PAS, uno di livello grave in cui il genitore alienante è il padre e il secondo di livello medio-lieve in cui il genitore programmatore è la madre
The GRAAL of carpooling: GReen And sociAL optimization from crowd-sourced data
No full textCarpooling, i.e. the sharing of vehicles to reach common destinations, is often performed to reduce costs and pollution. Recent work on carpooling takes into account, besides mobility matches, also social aspects and, more generally, non-monetary incentives. In line with this, we present GRAAL, a data-driven methodology for GReen And sociAL carpooling. GRAAL optimizes a carpooling system not only by minimizing the number of cars needed at the city level, but also by maximizing the enjoyability of people sharing a trip. We introduce a measure of enjoyability based on people's interests, social links, and tendency to connect to people with similar or dissimilar interests. GRAAL computes the enjoyability within a set of users from crowd-sourced data, and then uses it on real world datasets to optimize a weighted linear combination of number of cars and enjoyability. To tune this weight, and to investigate the users’ interest on the social aspects of carpooling, we conducted an online survey on potential carpooling users. We present the results of applying GRAAL on real world crowd-sourced data from the cities of Rome and San Francisco. Computational results are presented from both the city and the user perspective. Using the crowd-sourced weight, GRAAL is able to significantly reduce the number of cars needed, while keeping a high level of enjoyability on the tested data-set. From the user perspective, we show how the entire per-car distribution of enjoyability is increased with respect to the baselines
Detecting Autism by Analyzing a Simulated Social Interaction
No full textDrimalla H, Landwehr N, Baskow I, et al. Detecting Autism by Analyzing a Simulated Social Interaction. In: Berlingerio M, Bonchi F, Gärtner T, Hurley N, Ifrim G, eds. Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2018, Dublin, Ireland, September 10–14, 2018, Proceedings, Part I. Lecture Notes in Computer Science. Vol 11051. Cham: Springer International Publishing; 2019: 193-208
