TRAP: using TaRgeted Ads to unveil Google personal Profiles

In the last decade, the advertisement market spread significantly in the web and mobile app system. Its effectiveness is also due thanks to the possibility to target the advertisement on the specific interests of the actual user, other than on the content of the website hosting the advertisement. In this scenario, became of great value services that collect and hence can provide information about the browsing user, like Facebook and Google. In this paper, we show how to maliciously exploit the Google Targeted Advertising system to infer personal information in Google user profiles. In particular, the attack we consider is external from Google and relies on combining data from Google AdWords with other data collected from a website of the Google Display Network. We validate the effectiveness of our proposed attack, also discussing possible application scenarios. The result of our research shows a significant practical privacy issue behind such type of targeted advertising service, and call for further investigation and the design of more privacy-aware solutions, possibly without impeding ?the current business model involved in online advertisement.

Why Snoopy Loves Online Services: An Analysis of (Lack of) Privacy in Online Services

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ENEA PAES: A Web Platform for Supporting Italian Municipalities in Sustainable Energy Action Plan

The Covenant of Mayors promotes the Sustainable Energy Action Plan (SEAP), aiming to mitigate greenhouse gas (GHG) emissions in line with the European Union’s 2030 and 2050 targets. The Covenant signatories could take enormous advantage from a digital platform that allows SEAP drafting also to no technically skilled users, like majority of them are. The Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA) has developed the PAES platform in order to provide digital support to public administrations (PA) adhering to the Covenant of Mayors. The platform exploits open data and it is fed by energetic data aggregated on a municipal level. The platform offers appropriate functionalities for baseline CO2 emissions inventory (BEI) filling out and a best practice (BP) simulation tool. The latter allows to contextualize each BP and to estimate its effects in terms of the main GHG emission. The BP showing the best estimation results can then be converted into concrete adaptation actions. So, this digital system facilitates local Italian municipalities in the strategic planning and monitoring of adaptation actions taken over time

A study on text-score disagreement in online reviews

In this paper, we focus on online reviews and employ artificial intelligence tools, taken from the cognitive computing field, to help understanding the relationships between the textual part of the review and the assigned numerical score. We move from the intuitions that 1) a set of textual reviews expressing different sentiments may feature the same score (and vice-versa); and 2) detecting and analyzing the mismatches between the review content and the actual score may benefit both service providers and consumers, by highlighting specific factors of satisfaction (and dissatisfaction) in texts. To prove the intuitions, we adopt sentiment analysis techniques and we concentrate on hotel reviews, to find polarity mismatches therein. In particular, we first train a text classifier with a set of annotated hotel reviews, taken from the Booking website. Then, we analyze a large dataset, with around 160k hotel reviews collected from Tripadvisor, with the aim of detecting a polarity mismatch, indicating if the textual content of the review is in line, or not, with the associated score. Using well established artificial intelligence techniques and analyzing in depth the reviews featuring a mismatch between the text polarity and the score, we find that -on a scale of five stars- those reviews ranked with middle scores include a mixture of positive and negative aspects. The approach proposed here, beside acting as a polarity detector, provides an effective selection of reviews -on an initial very large dataset- that may allow both consumers and providers to focus directly on the review subset featuring a text/score disagreement, which conveniently convey to the user a summary of positive and negative features of the review target.Comment: This is the accepted version of the paper. The final version will be published in the Journal of Cognitive Computation, available at Springer via http://dx.doi.org/10.1007/s12559-017-9496-

Implementation of a framework for graph-based keyword search over relational data

The challenge of easily interconnecting and exploiting the increasing amount of data from structured data sources is still a primary concern for researchers from the industry and the academy. Keyword search over structured data systems (KSS) has attracted much interest as it provides a simple interface to query structured data. At the best of our knowledge, KSS evolved neither into a standard model nor into a commercial product. The implementation of every new system published so far was isolated from the previous systems, even when it advanced the state of the art for a single aspect. Also, the source code of these systems is not shared and the experimental results are not easily replicable. The present study aims at filling this gap, by the design and the shared implementation of a unified framework for graph-based KSS

Towards a framework for graph-based keyword search over relational data

Keyword-based access to structured data has attracted research and industry as a means for facilitating access to information. In recent years, the research community and big data technology vendors put a lot of effort into developing new proof of concept systems for the task at hand. Two major limitations have been identified for such prototypes to transition into fully developed products: 1) systems are not designed to scale up; 2) the absence of a complete evaluation approach oriented towards effectiveness. This work presents a framework for supporting the development and the evaluation of graph-based keyword search systems. Furthermore, the implementation of a core module of this framework is detailed and shared open-source with the community

Percorso didattico sulla ricorsione dalla natura al coding

Il presente lavoro è un percorso educativo didattico destinato ad una classe terza di un Liceo Scientifico. Partendo dall’osservazione di casi di ricorsione presenti in natura, e poi in quasi tutte le discipline umane, giunge alla sua definizione formale. L’obiettivo è insegnare agli allievi l’applicazione della ricorsione, come tecnica di programmazione alternativa, e il suo potere espressivo

Detection of Similar Terrorist Events

Abstract. Event counting is significant when it allows us to discover and represent implicit knowledge. We realize that a particular event happens somewhere not just by mere chance, it is unlikely to be what we call as accidental event. E.g. the number of violent attacks and terrorist acts can give the measure of the safety for a given country and can help us to predict where and/or when similar events are likely to happen next time. This work proposes an approach for detecting terrorist events sharing common details, available from open datasets, with the aim of merging their descriptions and counting them exactly. Events are aggregated according to a space-timetextual similarity function.

MIB at SemEval-2016 Task 4a: Exploiting lexicon-based features for sentiment analysis in Twitter

This work presents our team solution for task 4a (Message Polarity Classification) at the SemEval 2016 challenge. Our experiments have been carried out over the Twitter dataset provided by the challenge. We follow a supervised approach, exploiting a SVM polynomial kernel classifier trained with the challenge data. The classifier takes as input advanced NLP features. This paper details the features and discusses the achieved results

THE HIXOSFS MUSIC APPROACH VS COMMON MUSICAL FILE MANAGEMENT SOLUTIONS

Hixosfs music is an extension of ext2 Linux filesystem, with additional features to easy accessing and categorizing musical files. Specially it extends the inode struct inside the Virtual file system, considerating as file proprieties meta information such as album, author, title related to the content of a musical file. Comparition have been done respect to the Linux file system in user space Musicmeshfs, the Linux ext2 xattr feature, and ad hoc user space programs for efficiently retriving multimedia data. Since Hixosfs music manages the musical tags at kernel level, it offers higher performances then the other solutions but with less flexibility