Specifying and analysing reputation systems with coordination languages

Reputation systems are nowadays widely used to support decision making in networked systems. Parties in such systems rate each other and use shared ratings to compute reputation scores that drive their interactions. The existence of reputation systems with remarkable differences calls for formal approaches to their analysis. We present a verification methodology for reputation systems that is based on the use of the coordination language Klaim and related analysis tools. First, we define a parametric Klaim specification of a reputation system that can be instantiated with different reputation models. Then, we consider stochastic specification obtained by considering actions with random (exponentially distributed) duration. The resulting specification enables quantitative analysis of properties of the considered system. Feasibility and effectiveness of our proposal is demonstrated by reporting on the analysis of two reputation models

Multi-GPU aggregation-based AMG preconditioner for iterative linear solvers

We present and release in open source format a sparse linear solver which efficiently exploits heterogeneous parallel computers. The solver can be easily integrated into scientific applications that need to solve large and sparse linear systems on modern parallel computers made of hybrid nodes hosting NVIDIA Graphics Processing Unit (GPU) accelerators. The work extends our previous efforts in the exploitation of a single GPU accelerator and proposes an implementation, based on the hybrid MPI-CUDA software environment, of a Krylov-type linear solver relying on an efficient Algebraic MultiGrid (AMG) preconditioner already available in the BootCMatchG library. Our design for the hybrid implementation has been driven by the best practices for minimizing data communication overhead when multiple GPUs are employed, yet preserving the efficiency of the single GPU kernels. Strong and weak scalability results on well-known benchmark test cases of the new version of the library are discussed. Comparisons with the Nvidia AmgX solution show an improvement of up to 2.0x in the solve phase

Reputation-based Cooperation in the Clouds

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 comput- ing 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 dis- closure the details of their resources. Lacking such information, we advo- cate to rely on reputation scores obtained by evaluating the interactions among participants. More specifically, we propose a methodology to as- sess, at design time, the impact of different (reputation-based) collabo- rator selection strategies on the system performance. The evaluation is performed through statistical analysis on a volunteer cloud simulator

Onion under Microscope: An in-depth analysis of the Tor network

Tor is an anonymity network that allows offering and accessing various kinds of resources, known as hidden services, while guaranteeing sender and receiver anonymity. The Tor web is the set of web resources that exist on the Tor network, and Tor websites are part of the so-called dark web. Recent research works have evaluated Tor security, evolution over time, and thematic organization. Nevertheless, few information are available about the structure of the graph defined by the network of Tor websites. The limited number of Tor entry points that can be used to crawl the network renders the study of this graph far from being simple. In this paper we aim at better characterizing the Tor Web by analyzing three crawling datasets collected over a five-month time frame. On the one hand, we extensively study the global properties of the Tor Web, considering two different graph representations and verifying the impact of Tor's renowned volatility. We present an in depth investigation of the key features of the Tor Web graph showing what makes it different from the surface Web graph. On the other hand, we assess the relationship between contents and structural features. We analyse the local properties of the Tor Web to better characterize the role different services play in the network and to understand to which extent topological features are related to the contents of a service

Inferring urban social networks from publicly available data

The emergence of social networks and the definition of suitable generative models for synthetic yet realistic social graphs are widely studied problems in the literature. By not being tied to any real data, random graph models cannot capture all the subtleties of real networks and are inadequate for many practical contexts -- including areas of research, such as computational epidemiology, which are recently high on the agenda. At the same time, the so-called contact networks describe interactions, rather than relationships, and are strongly dependent on the application and on the size and quality of the sample data used to infer them. To fill the gap between these two approaches, we present a data-driven model for urban social networks, implemented and released as open source software. Given a territory of interest, and only based on widely available aggregated demographic and social-mixing data, we construct an age-stratified and geo-referenced synthetic population whose individuals are connected by "strong ties" of two types: intra-household (e.g., kinship) or friendship. While household links are entirely data-driven, we propose a parametric probabilistic model for friendship, based on the assumption that distances and age differences play a role, and that not all individuals are equally sociable. The demographic and geographic factors governing the structure of the obtained network, under different configurations, are thoroughly studied through extensive simulations focused on three Italian cities of different size

Reputation-based composition of social web services

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. © 2014 IEEE

On the analysis and evaluation of trust and reputation systems

In recent years, we have witnessed an increasing use of trust and reputation systems in different areas of ICT. The idea at the base of trust and reputation systems is of letting users to rate the provided services after each interaction. Other users may use aggregate ratings to compute reputation scores for a given party. The computed reputation scores are a collective measure of parties trustworthiness and are used to drive parties interactions. Due to the widespread use of reputation systems, research work on them is intensifying and several models have been proposed. This calls for a methodology for the analysis and the evaluation of trust and reputation systems that can help researcher and developers in studying, designing and implementing such systems. In this thesis we propose different kinds of theoretical results and software tools that could be useful means for researchers and developers in area of trust and reputation systems. Our work addresses the three main stages of trust and reputation systems development, namely study, design and implementation. We provide: 1) a general framework based on Bayesian decision theory for the assessment of trust and reputation models, 2) an analysis methodology for reputation systems based on a coordination language, 3) a software tool for network-aware evaluation of reputation systems and their rapid prototyping

Characterizing networks of propaganda on twitter: a case study

Abstract The daily exposure of social media users to propaganda and disinformation campaigns has reinvigorated the need to investigate the local and global patterns of diffusion of different (mis)information content on social media. Echo chambers and influencers are often deemed responsible of both the polarization of users in online social networks and the success of propaganda and disinformation campaigns. This article adopts a data-driven approach to investigate the structuration of communities and propaganda networks on Twitter in order to assess the correctness of these imputations. In particular, the work aims at characterizing networks of propaganda extracted from a Twitter dataset by combining the information gained by three different classification approaches, focused respectively on (i) using Tweets content to infer the “polarization” of users around a specific topic, (ii) identifying users having an active role in the diffusion of different propaganda and disinformation items, and (iii) analyzing social ties to identify topological clusters and users playing a “central” role in the network. The work identifies highly partisan community structures along political alignments; furthermore, centrality metrics proved to be very informative to detect the most active users in the network and to distinguish users playing different roles; finally, polarization and clustering structure of the retweet graphs provided useful insights about relevant properties of users exposure, interactions, and participation to different propaganda items.info:eu-repo/semantics/publishe

Metodi di studio ed ottimizzazione utili nel processo ceramico. La previsione del ritiro ceramico ed applicazioni in campo industriale

E' stata mostrata la possibilit\ue0 di raggiungere rapidamente l'ottimizzazione di formulazioni d'impasti con evidenti vantaggi economici e qualitativi. I metodi proposti sono quelli della regressione lineare a pi\uf9 variabili e dell'approccio neurale. Entrambe consentono di ottenere un modello matematico col quale simulare virtualmente il comportamento del sistema in diverse condizioni. Quest'ultimo viene ottenuto utilizzando i dati storici presenti in azienda oppure da dati sperimentali. Nell'intervento \ue8 stata mostrato un esempio applicato agli impasti: \uec modelli sono stati costruiti sui dati di ritiro in relazione alla composizione, espressa comepercentuale delle singole materie prime, e alla temperatura massima di cottura. I modelli sono stati testati con venti formulazioni di prova mostrando una ottima capacit\ue0 predittiva. La disponibilit\ue0 di softwares in grado d\uec eseguire i calcoli necessari e la ridotta richiesta di conoscenze matematiche rende le due metodologie di facile e rapida introduzione in ambito industriale.Based on the linear regression with variables and artificial neural network, a math. model was developed enabling the simulation of the system behavior under different conditions. Existing historical data or through test results were employed in obtaining the model. As an example, the capability of the model to predict the shrinkage in processing of ceramic materials, i.e. between the dried state and after heat treatment cycles, was shown, the compn. expressed as raw materials percentage, and max. firing temp. being taken into account. The models were tested with twenty different formulas and showed excellent predictive capacity. The availability of software packages that could perform all necessary calcns. and the little math. knowledge enabled the easy and rapid introduction of the developed model in industrial processes