Learning a Macroscopic Model of Cultural Dynamics

A fundamental open question that has been studied by sociologists since the 70s and recently started being addressed by the computer-science community is the understanding of the role that influence and selection play in shaping the evolution of socio-cultural systems. Quantifying these forces in real settings is still a big challenge, especially in the large-scale case in which the entire social network between the users may not be known, and only longitudinal data in terms of masses of cultural groups (e.g., political affiliation, product adoption, market share, cultural tastes) may be available. We propose an influence and selection model encompassing an explicit characterization of the feature space for the different cultural groups in the form of a natural equation-based macroscopic model, following the approach of Kempe et al. [EC 2013]. Our main goal is to estimate edge influence strengths and selection parameters from an observed time series. To do an experimental evaluation on real data, we perform learning on real datasets from Last. FM and Wikipedia

Building an Emulation Environment for Cyber Security Analyses of Complex Networked Systems

Computer networks are undergoing a phenomenal growth, driven by the rapidly increasing number of nodes constituting the networks. At the same time, the number of security threats on Internet and intranet networks is constantly growing, and the testing and experimentation of cyber defense solutions requires the availability of separate, test environments that best emulate the complexity of a real system. Such environments support the deployment and monitoring of complex mission-driven network scenarios, thus enabling the study of cyber defense strategies under real and controllable traffic and attack scenarios. In this paper, we propose a methodology that makes use of a combination of techniques of network and security assessment, and the use of cloud technologies to build an emulation environment with adjustable degree of affinity with respect to actual reference networks or planned systems. As a byproduct, starting from a specific study case, we collected a dataset consisting of complete network traces comprising benign and malicious traffic, which is feature-rich and publicly available

Targeted interest-driven advertising in cities using Twitter

Targeted advertising is a key characteristic of online as well as traditional-media marketing. However it is very limited in outdoor advertising, that is, performing campaigns by means of billboards in public places. The reason is the lack of information about the interests of the particular passersby, except at very imprecise and aggregate demographic or traffic estimates. In this work we propose a methodology for performing targeted outdoor advertising by leveraging the use of social media. In particular, we use the Twitter social network to gather information about users’ degree of interest in given advertising categories and about the common routes that they follow, characterizing in this way each zone in a given city. Then we use our characterization for recommending physical locations for advertising. Given an advertisement category, we estimate the most promising areas to be selected for the placement of an ad that can maximize its targeted effectiveness. We show that our approach is able to select advertising locations better with respect to a baseline reflecting a current ad-placement policy. To the best of our knowledge this is the first work on offline advertising in urban areas making use of (publicly available) data from social networks

Similarity Search for Dynamic Data Streams

Nearest-neighbor searching systems are an integral part of many online applications, including but not limited to pattern recognition, plagiarism detection and recommender systems. With increasingly larger data sets, scalability has become an important issue. Many of the most space and running time efficient algorithms are based on locality sensitive hashing. The de facto standard approach to quickly answer nearest-neighbor queries on such a data set is usually a form of min-hashing. Not only is min-hashing very fast, but it is also space efficient and can be implemented in many computational models aimed at dealing with large data sets such as MapReduce and streaming. A significant drawback is that minhashing and related methods are only able to handle insertions to user profiles and tend to perform poorly when items may be removed. We initiate the study of scalable locality sensitive hashing (LSH) for dynamic data-streams. Specifically, using the Jaccard index as similarity measure, we design (1) a nearest-neighbor datastructure maintainable in dynamic data streams and (2) a sketching algorithm for similarity estimation. Our algorithms have little overhead in terms of running time compared to previous LSH approaches for the insertion streams, and drastically outperform previous algorithms in case of deletion

Targeted Interest-Driven Advertising in Cities Using Twitter

Targeted advertising is a key characteristic of online as well as traditional-media marketing. However, it is very limited in outdoor advertising, that is, performing campaigns by means of billboards in public places. In this work we propose a methodology for performing targeted outdoor advertising by leveraging the use of social media. In particular, we use the Twitter social network to gather information about users' degree of interest in given advertising categories and about the routes that they follow. Given an advertising category, we estimate the most promising areas to be selected for the placement of an ad that can maximize its targeted effectiveness

Sketch 'Em All: Fast Approximate Similarity Search for Dynamic Data Streams

Recommender systems are an integral part of many web applica- tions. With increasingly larger user bases, scalability has become an important issue. Many of the most scalable algorithms with respect to both space and running times are based on locality-sensitive hashing (LSH). However, a significant drawback is that these meth- ods are only able to handle insertions to user profiles and tend to perform poorly when items may be removed. We initiate the study of scalable locality-sensitive hashing for dynamic input. Specifi- cally, using the Jaccard index as similarity measure, we design (1) a sketching algorithm for similarity estimation via a black box re- duction to ℓ0 norm estimation and (2) a locality sensitive hashing scheme maintainable in fully dynamic data streams that quickly filters out low-similarity pairs. Our algorithms have little to no overhead in terms of running time compared to previous LSH ap- proaches for the insertion only case, and drastically outperform previous algorithms in case of deletion