Social Web Services (SWSs) constitute a novel paradigm of service-oriented computing, where Web services, just like humans, sign up in social networks that guarantee, e.g., better service discovery for users and faster replacement in case of service failures. In past work, composition of SWSs was mainly supported by specialised social networks of competitor services and cooperating ones. In this work, we continue this line of research, by proposing a novel SWSs composition procedure driven by the SWSs reputation. Making use of a well-known formal language and associated tools, we specify the composition steps and we prove that such reputation-driven approach assures better results in terms of the overall quality of service of the compositions, with respect to randomly selecting SWSs.

The popularity of the cloud computing paradigm is opening new opportunities for collaborative computing. In this paper we tackle a fundamental problem in open-ended cloud-based distributed computing platforms, i.e., the quest for potential collaborators. We assume that cloud participants are willing to share their computational resources for shared distributed computing problems, but they are not willing to disclose the details of their resources. Lacking such information, we advocate to rely on reputation scores obtained by evaluating the interactions among participants. More specifically, we propose a methodology to assess, at design time, the impact of different (reputation-based) collaborator selection strategies on the system performance. The evaluation is performed through statistical analysis on a volunteer cloud simulator.

Two conflicting high-level goals govern the enforcement of security policies, abridged in the phrase ``high security at a low cost''. While these drivers seem irreconcilable, formal modelling languages and automated verification techniques can facilitate the task of finding the right balance. We propose a modelling language and a framework in which security checks can be relaxed or strengthened to save resources or increase protection, on the basis of trust relationships among communicating parties. Such relationships are automatically derived through a reputation system, hence adapt dynamically to the observed behaviour of the parties and are not fixed a priori. In order to evaluate the impact of the approach, we encode our modelling language in StoKlaim, which enables verification via the dedicated statistical model checker SAM. The overall approach is applied to a fragment of a Wireless Sensor Network, where there is a clear tension between devices with limited resources and the cost for securing the communication.

In recent years we are witnessing a continuous growth in the amount of data that both public and private organizations collect and profit by. Search engines are the most common tools used to retrieve information, and more recently, clustering techniques showed to be an effective tool in helping users to skim query results. The majority of the systems proposed to manage information, provide textual interfaces to explore search results that are not specifically designed to provide an interactive experience to the users. Trying to find a solution to this problem, we focus on how to extract conveniently data from sources of interest, and how to enhance their analysis and consultation through visualization techniques. In this work we present a customizable framework able to acquire, search and interactively visualize data. This framework is built upon a modular architectural schema and its effectiveness will be illustrated by a prototype implemented for a specific application domain.

In this paper we introduce new variables that can be used to retrieve the atmospheric continuum emission in the inversion of remote sensing measurements. This modification tackles the so-called sloppy model problem. We test this approach on an extensive set of real measurements from the Michelson Interferometer for Passive Atmospheric Sounding. The newly introduced variables permit to achieve a more stable inversion and a smaller value of the minimum of the cost function.

The upcoming launch of the next generation of hyperspectral satellites (PRISMA, EnMap, HyspIRI, etc.) will meet the increasing demand for the availability/accessibility of hyperspectral information on agricultural land use from the agriculture community. To this purpose, algorithms for the classification of remotely sensed images are here considered for agricultural monitoring of cultivated area, exploiting remotely sensed high spectral resolution images. Classification is accomplished by procedures based on discriminant analysis tools that well suit hyperspectrality, circumventing what in statistics is called "the curse of dimensionality". As a byproduct of classification, a full assessment of the spectral bands of the sensor is obtained, ranking them with the purpose of understanding their role in segmentation and classification. The methodology has been validated on two independent image datasets gathered by the MIVIS (Multispectral Infrared and Visible Imaging Spectrometer) sensor for which ground validations were available. A comparison with the popular multiclass SVM (Support Vector Machines) classifier is also presented. Results show that a good classification (minimum global success rate 95% through all experiments) is achieved by using the 10 spectral bands selected as the most discriminant by the proposed procedure; moreover, it also appears that nonparametric techniques generally outperform parametric ones. The present study confirms that the new generation of hyperspectral satellite data like PRISMA can ripen an end-user application for agricultural land-use of cultivated area.

We study the Stekloff eigenvalue problem for the so-called pseudo p-Laplacian operator. After proving the existence of an unbounded sequence of eigenvalues, we focus on the first nontrivial eigenvalue ?, providing various equivalent characterizations for it. We also prove an upper bound for ? in terms of geometric quantities. The latter can be seen as the nonlinear analogue of the Brock-Weinstock inequality for the first nontrivial Stekloff eigenvalue of the (standard) Laplacian. Such an estimate is obtained by exploiting a family of sharp weighted Wulff inequalities, which are here derived and appear to be interesting in themselves. © 2013 Springer Basel.

Given an open set ?, we consider the problem of providing sharp lower bounds for ? (?), i.e. its second Dirichlet eigenvalue of the p-Laplace operator. After presenting the nonlinear analogue of the Hong-Krahn-Szego inequality, asserting that the disjoint unions of two equal balls minimize ? among open sets of given measure, we improve this spectral inequality by means of a quantitative stability estimate. The extremal cases p = 1 and p = ? are considered as well. © 2012 Springer-Verlag Berlin Heidelberg.

A highly nonlinear eigenvalue problem is studied in a Sobolev space with variable exponent. The Euler-Lagrange equation for the minimization of a Rayleigh quotient of two Luxemburg norms is derived. The asymptotic case with a "variable infinity" is treated. Local uniqueness is proved for the viscosity solutions.

This chapter concerns the state-of-the-art of the research about non-local minimal surfaces and their phase-field approximations.

We present computer simulations of the response of a flexoelectric blue phase network, either in bulk or under confinement, to an applied field. We find a transition in the bulk between the blue phase I disclination network and a parallel array of disclinations along the direction of the applied field. Upon switching off the field, the system is unable to reconstruct the original blue phase but gets stuck in a metastable phase. Blue phase II is comparatively much less affected by the field. In confined samples, the anchoring at the walls and the geometry of the device lead to the stabilisation of further structures, including field-aligned disclination loops, splayed nematic patterns, and yet more metastable states. Our results are relevant to the understanding of the switching dynamics for a class of new, "superstable", blue phases which are composed of bimesogenic liquid crystals, as these materials combine anomalously large flexoelectric coefficients, and low or near-zero dielectric anisotropy.

Active Brownian particles (ABPs), when subject to purely repulsive interactions, are known to undergo activity-induced phase separation broadly resembling an equilibrium (attraction-induced) gas-liquid coexistence. Here we present an accurate continuum theory for the dynamics of phase-separating ABPs, derived by direct coarse graining, capturing leading-order density gradient terms alongside an effective bulk free energy. Such gradient terms do not obey detailed balance; yet we find coarsening dynamics closely resembling that of equilibrium phase separation. Our continuum theory is numerically compared to large-scale direct simulations of ABPs and accurately accounts for domain growth kinetics, domain topologies, and coexistence densities.

In Mobile Unattended Wireless Sensor Networks (MUWSNs), nodes sense the environment and store the acquired data until the arrival of a trusted data sink. In this paper, we address the fundamental issue of quantifying to which extent secret sharing schemes, combined with nodes mobility, can help in assuring data availability and confidentiality. We provide accurate analytical results binding the fraction of the network accessed by the sink and the adversary to the amount of information they can successfully recover. Extensive simulations support our findings.

In Mobile Unattended Wireless Sensor Networks (MUWSNs), nodes sense the environment and store the acquired data until the arrival of a trusted data sink. MUWSNs, other than being a reference model for an increasing number of military and civilian applications, also capture a few important characteristics of emerging computing paradigms like Participatory Sensing (PS). In this paper, we start by identifying the main features and issues of MUWSNs, revising the related work in the area and highlighting their shortcomings. We then propose a new approach based on secret sharing and information diffusion to improve data integrity and confidentiality, and present experimental results confirming the effectiveness of this solution. The rationale is that information sharing among neighboring nodes, combined with nodes mobility, helps distributing the information into the network in a way that, at the same time, facilitates data recovering and is resilient to data loss or stealing. This is a first step towards the general objective of providing closed results about how secret sharing schemes and nodes mobility can help in assuring data security using local communications only, and understanding how to set system parameters to achieve the desired trade-off between confidentiality and availability.

An important problem in the context of wireless sensor networks is the Maximum Network Lifetime Problem (MLP): find a collection of subset of sensors (cover) each covering the whole set of targets and assign them an activation time so that network lifetime is maximized. In this paper we consider a variant of MLP, where we allow each cover to neglect a certain fraction (1 - ?) of the targets. We analyze the problem and show that the total network lifetime can be hugely improved by neglecting a very small portion of the targets. An exact approach, based on a Column Generation scheme, is presented and a heuristic solution algorithm is also provided to initialize the approach. The proposed approaches are tested on a wide set of instances. The experimentation shows the effectiveness of both the proposed problems and solution algorithms in extending network lifetime and improving target coverage time when some regularity conditions are taken into account. © 2011 Springer-Verlag.

In the colourful travelling salesman problem (CTSP), given a graph G with a (not necessarily distinct) label (colour) assigned to each edge, a Hamiltonian tour with the minimum number of different labels is sought. The problem is a variant of the well-known Hamiltonian cycle problem and has potential applications in telecommunication networks, optical networks, and multimodal transportation networks, in which one aims to ensure connectivity or other properties by means of a limited number of connection types. We propose two new heuristics based on the deconstruction of a Hamiltonian tour into subpaths and their reconstruction into a new tour, as well as an adaptation of an existing approach. Extensive experimentation shows the effectiveness of the proposed approaches.

A technique for color quantization is described, which consists of two processes. The first process is based on the analysis of the histograms of the three color components of the RGB input image. The second process performs clustering of the colors quantized by the first process, based on their Euclidean distance. At the end of the second process, the output image is obtained by replacing the color of each pixel of the input image with the closest representative color. The obtained results are satisfactory from both the qualitative and the quantitative point of view.