TRULLO - local trust bootstrapping for ubiquitous devices

Handheld devices have become sufficiently powerful that it is easy to create, disseminate, and access digital content (e.g., photos, videos) using them. The volume of such content is growing rapidly and, from the perspective of each user, selecting relevant content is key. To this end, each user may run a trust model - a software agent that keeps track of who disseminates content that its user finds relevant. This agent does so by assigning an initial trust value to each producer for a specific category (context); then, whenever it receives new content, the agent rates the content and accordingly updates its trust value for the producer in the content category. However, a problem with such an approach is that, as the number of content categories increases, so does the number of trust values to be initially set. This paper focuses on how to effectively set initial trust values. The most sophisticated of the current solutions employ predefined context ontologies, using which initial trust in a given context is set based on that already held in similar contexts. However, universally accepted (and time invariant) ontologies are rarely found in practice. For this reason, we propose a mechanism called TRULLO (TRUst bootstrapping by Latently Lifting cOntext) that assigns initial trust values based only on local information (on the ratings of its user’s past experiences) and that, as such, does not rely on third-party recommendations. We evaluate the effectiveness of TRULLO by simulating its use in an informal antique market setting. We also evaluate the computational cost of a J2ME implementation of TRULLO on a mobile phone

Nowcasting gentrification using Airbnb data

There is a rumbling debate over the impact of gentrification: presumed gentrifiers have been the target of protests and attacks in some cities, while they have been welcome as generators of new jobs and taxes in others. Census data fails to measure neighborhood change in real-time since it is usually updated every ten years. This work shows that Airbnb data can be used to quantify and track neighborhood changes. Specifically, we consider both structured data (e.g., number of listings, number of reviews, listing information) and unstructured data (e.g., user-generated reviews processed with natural language processing and machine learning algorithms) for three major cities, New York City (US), Los Angeles (US), and Greater London (UK). We find that Airbnb data (especially its unstructured part) appears to nowcast neighborhood gentrification, measured as changes in housing affordability and demographics. Overall, our results suggest that user-generated data from online platforms can be used to create socioeconomic indices to complement traditional measures that are less granular, not in real-time, and more costly to obtain

The Digital Life of Walkable Streets

Walkability has many health, environmental, and economic benefits. That is why web and mobile services have been offering ways of computing walkability scores of individual street segments. Those scores are generally computed from survey data and manual counting (of even trees). However, that is costly, owing to the high time, effort, and financial costs. To partly automate the computation of those scores, we explore the possibility of using the social media data of Flickr and Foursquare to automatically identify safe and walkable streets. We find that unsafe streets tend to be photographed during the day, while walkable streets are tagged with walkability-related keywords. These results open up practical opportunities (for, e.g., room booking services, urban route recommenders, and real-estate sites) and have theoretical implications for researchers who might resort to the use social media data to tackle previously unanswered questions in the area of walkability.Comment: 10 pages, 7 figures, Proceedings of International World Wide Web Conference (WWW 2015

Quantified Canine: Inferring Dog Personality From Wearables

Being able to assess dog personality can be used to, for example, match shelter dogs with future owners, and personalize dog activities. Such an assessment typically relies on experts or psychological scales administered to dog owners, both of which are costly. To tackle that challenge, we built a device called "Patchkeeper" that can be strapped on the pet's chest and measures activity through an accelerometer and a gyroscope. In an in-the-wild deployment involving 12 healthy dogs, we collected 1300 hours of sensor activity data and dog personality test results from two validated questionnaires. By matching these two datasets, we trained ten machine-learning classifiers that predicted dog personality from activity data, achieving AUCs in [0.63-0.90], suggesting the value of tracking the psychological signals of pets using wearable technologies.Comment: 26 pages, 9 figures, 4 table

Changes in the fecal microbiota associated with a broad‐spectrum antimicrobial administration in hospitalized neonatal foals with probiotics supplementation

There is a wide array of evidence across species that exposure to antibiotics is associated with dysbiosis, and due to their widespread use, this also raises concerns also in medicine. The study aimed to determine the changes on the fecal microbiota in hospitalized neonatal foals administered with broad‐spectrum antimicrobials and supplemented probiotics. Fecal samples were collected at hospital admission (Ta), at the end of the antimicrobial treatment (Te) and at discharge (Td). Feces were analysed by next‐generation sequencing of the 16S rRNA gene on Illumina MiSeq. Seven foals treated with IV ampicillin and amikacin/gentamicin were included. The mean age at Ta was 19 h, the mean treatment length was 7 days and the mean time between Te and Td was 4.3 days. Seven phyla were identified: Actinobacteria, Bacteroidetes, Firmicutes, Fusobacteria, Proteobacteria, TM7 and Verrucomicrobia. At Ta, Firmicutes (48.19%) and Proteobacteria (31.56%) were dominant. The alpha diversity decreased from Ta to Te, but it was the highest at Td. The beta diversity was higher at Ta than at Te and higher at Td than at Te. An increase in Akkermansia over time was detected. The results suggest that the intestinal microbiota of neonatal foals rapidly returns to a high diversity after treatment. It is possible that in foals, the effect of antimicrobials is strongly influenced or overshadowed by the time‐dependent changes in the developing gut microbiota

Microbial community dynamics in mother's milk and infant's mouth and gut in moderately preterm infants

Mother's own milk represents the optimal source for preterm infant nutrition, as it promotes immune defenses and gastrointestinal function, protects against necrotizing enterocolitis, improves long-term clinical outcome and is hypothesized to drive gut microbiota assembly. Preterm infants at birth usually do not receive their mother's milk directly from the breast, because active suckling and coordination between suckling, swallowing and breathing do not develop until 32-34 weeks gestational age, but actual breastfeeding is usually possible as they grow older. Here, we enrolled moderately preterm infants (gestational age 32-34 weeks) to longitudinally characterize mothers' milk and infants' gut and oral microbiomes, up to more than 200 days after birth, through 16S rRNA sequencing. This peculiar population offers the chance to disentangle the differential contribution of human milk feeding per se vs. actual breastfeeding in the development of infant microbiomes, that have both been acknowledged as crucial contributors to short and long-term infant health status. In this cohort, the milk microbiome composition seemed to change following the infant's latching to the mother's breast, shifting toward a more diverse microbial community dominated by typical oral microbes, i.e., Streptococcus and Rothia. Even if all infants in the present study were fed human milk, features typical of healthy, full term, exclusively breastfed infants, i.e., high percentages of Bifidobacterium and low abundances of Pseudomonas in fecal and oral samples, respectively, were detected in samples taken after actual breastfeeding started. These findings underline the importance of encouraging not only human milk feeding, but also an early start of actual breastfeeding in preterm infants, since the infant's latching to the mother's breast might constitute an independent factor helping the health-promoting assembly of the infant gut microbiome