Exploiting Recurring Patterns to Improve Scalability of Parking Availability Prediction Systems

Parking Guidance and Information (PGI) systems aim at supporting drivers in finding suitable parking spaces, also by predicting the availability at driver’s Estimated Time of Arrival (ETA), leveraging information about the general parking availability situation. To do these predictions, most of the proposals in the literature dealing with on-street parking need to train a model for each road segment, with significant scalability issues when deploying a city-wide PGI. By investigating a real dataset we found that on-street parking dynamics show a high temporal auto-correlation. In this paper we present a new processing pipeline that exploits these recurring trends to improve the scalability. The proposal includes two steps to reduce both the number of required models and training examples. The effectiveness of the proposed pipeline has been empirically assessed on a real dataset of on-street parking availability from San Francisco (USA). Results show that the proposal is able to provide parking predictions whose accuracy is comparable to state-of-the-art solutions based on one model per road segment, while requiring only a fraction of training costs, thus being more likely scalable to city-wide scenarios

The Pr.O.F. project: orienting late High School students to University methodology in Chemistry and other Sciences

The P.r.O.F. project has been presented at the ECTN meeting and positively evaluated. The project aims to help high school students in the transition to University. It covers the areas of Chemistry, Physics, Biology and Mathematics. In the Chemistry section, methodological issues were emphasized. The specific subject, selected jointly by University and School teachers, was Solutions and Solubility. Short and long-time tests were performed to check the effectiveness of the intervention

Semantic Techniques for Multi-Cloud Applications Portability and Interoperability

The composition of Cloud Services to satisfy customer requirements is a complex task, owing to the huge number of services that arecurrentlyavailable. TheadventofBigDataandInternetofThings(IoT),whichrelyonCloudresourcesforbetterperformances and scalability, is pushing researchers to find new solutions to the Cloud Services composition problem. In this paper a semanticbased representation of Application Patterns and Cloud Services is presented, with an example of its use in a typical distributed application, which shows how the proposed approach can be successfully employed for the discovery and composition of Cloud Services.

Effects of Shear, Defocus, and Wavefront Error on the Theoretical Performance of the Composite Infrared Spectrometer for Cassini

The combined effects on performance of shear between the two arms, defocus of die detector, and difference in wavefront between the two arms of a Fourier transform spectrometer using cube corner retroreflectors were investigated. Performance was characterized by the amplitude of the fringe signals coming from a detector as the path-length difference was scanned. A closed-form expression was found for the combined effects of shear and defocus, and it was found that defocus had no effect in the absence of shear. The effect of wavefront error was modeled numerically and assumed to be independent of shear and defocus. Results were compared with measurements made on the breadboard and engineering model of the Composite Infrared Spectrometer for the Cassini mission to Saturn, and good agreement was found

A Small World of Bad Guys: Investigating the Behavior of Hacker Groups in Cyber-Attacks

This paper explores the behaviour of malicious hacker groups operating in cyberspace and how they organize themselves in structured networks. To better understand these groups, the paper uses Social Network Analysis (SNA) to analyse the interactions and relationships among several malicious hacker groups. The study uses a tested dataset as its primary source, providing an empirical analysis of the cooperative behaviours exhibited by these groups. The study found that malicious hacker groups tend to form close-knit networks where they consult, coordinate with, and assist each other in carrying out their attacks. The study also identified a "small world" phenomenon within the population of malicious actors, which suggests that these groups establish interconnected relationships to facilitate their malicious operations. The small world phenomenon indicates that the actor-groups are densely connected, but they also have a small number of connections to other groups, allowing for efficient communication and coordination of their activities