Online social networks expose their users to privacy leakage risks. To measure the risk, privacy scores can be computed to quantify the users’ profile exposure according to their privacy preferences or attitude. However, user privacy can be also influenced by external factors (e.g., the relative risk of the network, the position of the user within the social graph), but state-of-the-art scores do not consider such properties adequately. We define a network-aware privacy score that improves the measurement of user privacy risk according to the characteristics of the network. We assume that users that lie in an unsafe portion of the network are more at risk than users that are mostly surrounded by privacy-aware friends. The effectiveness of our measure is analyzed by means of extensive experiments on two simulated networks and a large graph of real social network users.

During our digital social life, we share terabytes of information that can potentially reveal private facts and personality traits to unexpected strangers. Despite the research efforts aiming at providing efficient solutions for the anonymization of huge databases (including networked data), in online social networks the most powerful privacy protection "weapons" are the users themselves. However, most users are not aware of the risks derived by the indiscriminate disclosure of their personal data. Moreover, even when social networking platforms allow their participants to control the privacy level of every published item, adopting a correct privacy policy is often an annoying and frustrating task and many users prefer to adopt simple but extreme strategies such as "visible-to-all" (exposing themselves to the highest risk), or "hidden-to-all" (wasting the positive social and economic potential of social networking websites). In this paper we propose a theoretical framework to i) measure the privacy risk of the users and alert them whenever their privacy is compromised and ii) help the users customize semi-automatically their privacy settings by limiting the number of manual operations. By investigating the relationship between the privacy measure and privacy preferences of real Facebook users, we show the effectiveness of our framework. (C) 2017 Elsevier Ltd. All rights reserved.

During our digital social life, we share terabytes of information that can potentially reveal private facts and personality traits to unexpected strangers. Despite the research efforts aiming at providing efficient solutions for the anonymization of huge databases (including networked data), in online social networks the most powerful privacy protection is in the hands of the users. However, most users are not aware of the risks derived by the indiscriminate disclosure of their personal data. With the aim of fostering their awareness on private data leakage risk, some measures have been proposed that quantify the privacy risk of each user. However, these measures do not capture the objective risk of users since they assume that all user’s direct social connections are close (thus trustworthy) friends. Since this assumption is too strong, in this paper we propose an alternative approach: each user decides which friends are allowed to see each profile item/post and our privacy score is defined accordingly. We show that it can be easily computed with minimal user intervention by leveraging an active learning approach. Finally, we validate our measure on a set of real Facebook users.

The risks due to a global and unaware diffusion of our personal data cannot be overlooked when more than two billion people are estimated to be registered in at least one of the most popular online social networks. As a consequence, privacy has become a primary concern among social network analysts and Web/data scientists. Some studies propose to "measure" users' profile privacy according to their privacy settings but do not consider the topological properties of the social network adequately. In this paper, we address this limitation and define a centrality-based privacy score to measure the objective user privacy risk according to the network properties. We analyze the effectiveness of our measures on a large network of real Facebook users.

The emerging need to make more attractive physical stores for catching new clients and maintaining the existing ones pushes retailers to develop efficient practices for acquiring knowledge from consumers and involving them in the points of sales' design. The final users' needs and preferences are considered a core in design process. This chapter proposes a system for involving consumers in the store design process through an innovative cloud participatory platform. It is a low cost hardware/software architecture offering a user-friendly interface able to be adopted by audiences with different background. Results show the consumers' interest to contribute in the design by using such technologies and providing a large amount of detailed information useful for future appealing stores' development. Finally, this chapter shows how the inclusion of modern low-cost game technologies in retail industry might provide ripper effects in several disciplines such as human-computer interaction, marketing, and management.

In this paper two approaches to solve the Poisson problem are presented and compared. The computational schemes are based on Smoothed Particle Hydrodynamics method which is able to perform an integral representation by means of a smoothing kernel function by involving domain particles in the discrete formulation.The first approach is derived by means of the variational formulation of the Poisson problem, while the second one is a direct differential method.Numerical examples on different domain geometries are implemented to verify and compare the proposed approaches; the computational efficiency of the developed methods is also studied. (C) 2012 Elsevier Inc. All rights reserved.

In this paper, a numerical meshless particle method is presented in order to solve the magnetoencephalography forward problem for analyzing the complex activation patterns in the human brain. The forward problem is devoted to compute the scalp potential and magnetic field distribution generated by a set of current sources representing the neural activity, and in this paper, it has been approached by means of the smoothed particle hydrodynamics method suitably handled. The Poisson equation generated by the quasi‐stationary Maxwell's curl equations, by assuming Neumann boundary conditions has been considered, and the current sources have been simulated by current dipoles. The adopted meshless particle model has provided good results in agreement with the analytical ones and by overcoming the drawback of the mesh generation. The numerical model has been validated, at first, in computing the electric potential and the external magnetic field for a dipole plunged near the upper surface of a homogeneous sphere simulating the human brain. Simulation results obtained by simulating two concentric spheres with different conductivities are also reported. Moreover, in order to better assess the validity of the proposed approach, a realistic human brain cortex model has been also simulated and compared with boundary element method results. A satisfactory agreement has been reached. Copyright © 2012 John Wiley & Sons, Ltd.

Many image processing techniques work with scattered data distribution usually employing grid based methods leading to numerical problems. To address this issue, a numerical method avoiding mesh generation can be used. Such a method performs an integral representation by means of a smoothing kernel function and, in the discrete formulation, involves domain particles. In this paper the meshless Smoothed Particle Hydrodynamics method is proposed in the Image Reconstruction context and a new computational strategy called Smoothed Particle Image Reconstruction is presented; the new method is based on a scatter approach and several innovative ideas are introduced in order to improve the computational efficiency and numerical accuracy. Tests are carried out validating the effectiveness of the approach.

Smoothed Particle Hydrodynamics is a meshless particle method able to evaluate unknown field functions and relative differential operators. This evaluation is done by performing an integral representation based on a suitable smoothing kernel function which, in the discrete formulation, involves a set of particles scattered in the problem domain. Two fundamental aspects strongly characterizing the development of the method are the smoothing kernel function and the particle distribution. Their choice could lead to the so-called particle inconsistency problem causing a loose of accuracy in the approximation; several corrective strategies can be adopted to overcome this problem. This paper focuses on the numerical behaviors of SPH with respect to the consistency restoring problem and to the particle distribution choice, providing useful hints on how these two aspects affect the goodness of the approximation and moreover how they mutually influence themselves. A series of numerical studies are performed approximating 1D, 2D and 3D functions validating this idea.

In this paper a novel approach for artificial mosaic generation is proposed. Gradient Vector Flow computation together with heuristics to maximise the covered mosaic area are used. The high frequency details are managed in a global way allowing to preserve the mosaic-style also for small ones. Experiments and comparisons with previous works confirm the effectiveness of the proposed algorithm.

Art often provides valuable hints for technological innovations especially in the field of Image Processing and Computer Graphics. In this paper we present a novel method to generate an artificial mosaic starting from a raster input image. This approach, based on Gradient Vector Flow computation and some smart heuristics, permit us to follow the most important edges maintaining at the same time high frequency details. Several examples and comparisons with other recent mosaic generation approaches show the effectiveness of our technique.

Thispaperdescribesan authoringsystemfor modelling Greekvirtual masks that expressemotions for synchronising their facial movementswith pre-recordedspeechfiles, and for creatingtheatrical performances.The systemis related to parametric modelling and includes three interfaces:Editor, Recorderand Virtual Theatre. In the Editor interface it is possible to create several different 3D masks from a unique mask basic model. In the Recorderit is possibleto import the models createdin the Editor and select eight expressions(neutral, anger,surprise,sadness, fear, joy, disgust, attention), as well as to create and save little alterations in these expressions,and to synchronise the facial movements with speech on the time-line of the system. In the Virtual Theatre the animated Greek masks can be imported and can perform.

Imaging techniques and applications often require heavy computations for finding the k-nearest-neighbour of a given pattern. Texture synthesis, image colourisation and super-resolution are all affected by this issue. Advanced clustering-based indexing schemas over metric spaces speed-up efficiently both k-nearest-neighbour and range searches. By using them, we are able to save CPU time without losing quality which would be lost using approximate approaches. Moreover, with the proposed technique we are able to convert a batch process task into a real-time task and, more importantly, it might be run on a typical user-end PC desktop rather than powerful mainframes. It has been shown how the application of recently reported well-known indexing schemas improves the speed performance of the above problems.

The paper proposes a new method devoted to identify specific semantic regions on CFA (Color Filtering Array) data images representing natural scenes. Making use of collected statistics over a large dataset of high quality natural images, the method uses spatial features and the Principal Component Analysis (PCA) in the HSL and normalized-RG color spaces. The classes considered, taking into account “visual significance”, are skin, vegetation, blue sky and sea. Semantic information are obtained on pixel basis leading to meaningful regions although not spatially coherent. Such information is used for automatic color rendition of natural digital images based on adaptive color correction. The overall method outperforms previous results providing reliable information validated by measured and subjective experiments.

In 1999 Chua demonstrated that it is possible to obtain boolean Cellular Automata (CA) by using Cellular Neural Networks (CNNs), implemented as chips, which definitely reduce the time of simulation for discrete dynamical systems such as CA and allow for a better understanding of complexity. In this work we demonstrate that Chua's Universal Neuron which simulates boolean CA can be generalized for multistate CA. This new approach allows to investigate the nature of the CA rule space, in relationship with the Universal Neuron parameter space, establishing relationships between discrete and continuous dynamical systems and analysing the nature of local and global rules in affecting the behaviour of these systems. The method which we have developed is fruitful since we have found a lot of CA which can be considered complex rules of class IV, in the Wolfram classification. Furthermore we have used the idea of genetical computation in considering the values of the Universal Neuron parameter space as a sort of genotype which determines and produces variations in the CA behaviour. On this basis, we have implemented a genetic algorithm which fits very well with the searching for CA complex rules.The nature of the relationship between continuous and discrete systems is a very important topic in the Science of Complexity which helps us to clarify the temporal dimension of many biological phenomena and processes.

Art often provides valuable hints for technological innovations especially in the field of Image Processing and Computer Graphics. In this paper we survey in a unified framework several methods to transform raster input images into good quality mosaics. For each of the major different approaches in literature the paper reports a short description and a discussion of the most relevant issues. To complete the survey comparisons among the different techniques both in terms of visual quality and computational complexity are provided.

We propose novel techniques for microarray image analysis. In particular, we describe an overall pipeline able to solve the most common problems of microarray image analysis. We propose the microarray image rotation algorithm (MIRA) and the statistical gridding pipeline (SGRIP) as two advanced modules devoted to restoring the original microarray grid orientation and to detecting, the correct geometrical information about each spot of input microarray, respectively. Both solutions work by making use of statistical observations, obtaining adaptive and reliable information about each spot property. They improve the performance of the microarray image segmentation pipeline (MISP) we recently developed. MIRA, MISP, and SGRIP modules have been developed as plug-ins for an advanced framework for microarray image analysis. A new quality measure able to effectively evaluate the adaptive segmentation with respect to the fixed (e.g., nonadaptive) circle segmentation of each spot is proposed. Experiments confirm the effectiveness of the proposed techniques in terms of visual and numerical data. (c) 2007 SPIE and IS&T.