The attraction of contributors in free and open source software projects

As firms increasingly sanction an open sourcing strategy, the question of which open source project to undertake remains tentative. The lack of established metrics makes it difficult to formulate such strategy. While many projects have been formed and created, only a few managed to remain active. With the majority of these projects failing, firms need a reliable set of criteria to assess what makes a project appealing not only to developers but also to visitors, users and commercial sponsors. In this paper, we develop a theoretical model to explore the contextual and causal factors of project attractiveness in inducing activities such as source code contribution, software maintenance, and usage. We test our model with data derived from more than 4000 projects spanning 4 years. Our main findings include that projects’ set of conditions such as license restrictiveness and their available resources provide the context that directly influence the amount of work activities observed in the projects. It was also found that indirect and unintended contributions such as recommending software, despite of being non-technical, cannot be ignored for project activeness, diffusion and sustainability. Finally, our analysis provide evidence that higher attractiveness leads to more code-related activities with the downside of slowing down responsiveness to address projects’ tasks, such as the implementation of new features and bug fixes. Our model underscores the significance of the reinforcing effects of attractiveness and work activities in open source projects, giving us the opportunity to discuss strategies to manage common traps such as the liability of newness. We conclude by discussing the applicability of the research model to other user-led initiatives.     

A data fusion and spatial data analysis approach for the estimation of wheat grain nitrogen uptake from satellite data

The selection of the optimal band combination for the estimation of specific crop variables is a key aspect in order to obtain reliable estimation of in-field variability from multi- and hyperspectral remote-sensing data. The selection of the bands is strongly influenced by the phenological stage of the crop at the acquisition time. In this work, the influence of the growing stage on the combination of spectral bands related to grain nitrogen (N) uptake in wheat was evaluated using multispectral (Satellite Pour l’Observation de la Terre – SPOT) and hyperspectral (Compact High Resolution Imaging Spectrometer – CHRIS-PROBA) satellite images at different growth stages over two wheat growth seasons in central Italy. In order to identify the more appropriate covariates (spectral bands) for each phenological stage, stepwise regression with backward selection was combined with stepwise variance inflation factors (VIFs) analysis and linear mixed effect model (LMEM). The results obtained in this study suggest that the spectral region most related to N uptake varies over the growing season of the wheat crop. For SPOT data, near-infrared (NIR) region was selected at all the phenological stages in both growing seasons, except for the latest stage, with low chlorophyll content due to the onset of senescence, in which the red band was selected. At stem elongation, the shortwave infrared (SWIR) band of SPOT data was also selected. At this stage, the best N estimation accuracy was obtained using an LMEM (root mean square error, RMSE = 0.012 t ha^?1). The inclusion of a spatial component in the estimation model by means of LMEMs provided a more accurate estimation than ordinary least square (OLS) models at all growth stages. The test carried out with CHRIS-PROBA data at the fourth stage confirmed the importance of NIR and in particular of the red-edge region for N uptake prediction. A novel methodology is proposed, which involves two crucial aspects in the context of the use of remote-sensing data in precision agriculture: i) the standardization of the spatial resolution for in-field and satellite data by a geostatistical data technique (data fusion); and ii) the selection of the most appropriate spectral bands for each phenological stage, taking into account both correlation with the target variable and collinearity.     

Product interface design: A participatory approach based on virtual reality

The usability of the user interface is a key aspect for the success of several industrial products. This assumption has led to the introduction of numerous design methodologies addressed to evaluate the user-friendliness of industrial products. Most of these methodologies follow the participatory design approach to involve the user in the design process. Virtual Reality is a valid tool to support Participatory Design, because it facilitates the collaboration among designers and users.The present study aims to evaluate the feasibility and the efficacy of an innovative Participatory Design approach where Virtual Reality plays a ‘double role’: a tool to evaluate the usability of the virtual product interface, and a communication channel that allows users to be directly involved in the design process as co-designers.In order to achieve these goals, we conducted three experiments: the purpose of the first experiment is to determine the influence of the virtual interface on the usability evaluation by comparing “user–real product” interaction and “user–virtual product” interaction. Subsequently, we tested the effectiveness of our approach with two experiments involving users (directly or through their participation in a focus group) in the redesign of a product user interface. The experiments were conducted with two typologies of consumer appliances: a microwave oven and a washing machine.     

A Unifying and Rigorous Shape from Shading Method Adapted to Realistic Data and Applications

We propose a new method for the Lambertian Shape From Shading (SFS) problem based on the notion of Crandall-Lions viscosity solution. This method has the advantage of requiring the knowledge of the solution (the surface to be reconstructed) only on some part of the boundary and/or of the singular set (the set of the points at maximal intensity). Moreover it unifies in an unique mathematical formulation the works of Rouy et al. [34, 50], Falcone et al. [21], Prados et al. [46, 48, 49], based on the notion of viscosity solutions and the work of Dupuis and Oliensis [17] dealing with classical solutions and value functions. Also, it allows to generalize their results to the “perspective SFS” problem recently simultaneously introduced in [13,46,55]. While the theoretical part has been developed in [44], in this paper we give some stability results and we describe numerical schemes for the SFS based on this method. We construct provably convergent and robust algorithms. Finally, we apply our SFS method to real images and we suggest some real-life applications.     

Bonferroni means with induced ordered weighted average operators

The induced ordered weighted average is an averaging aggregation operator that provides a parameterized family of aggregation operators between the minimum and the maximum. This paper presents some new generalizations by using Bonferroni means (BM) forming induced BM. The main advantage of this approach is the possibility of reordering the results according to complex ranking processes based on order-inducing variables. The work also presents some additional extensions by using the weighted ordered weighted average, immediate weights, and hybrid averages. Some further generalizations with generalized and quasi-arithmetic means are also developed to consider a wide range of particular cases including quadratic and geometric aggregations. The article also considers the applicability of the new approach in-group decision-making developing an application in sales forecasting.     

The “Always Best Packet Switching” architecture for SIP-based mobile multimedia services

This paper presents a distributed architecture for the provision of seamless and responsive mobile multimedia services. This architecture allows its user applications to use concurrently all the wireless network interface cards (NICs) a mobile terminal is equipped with. In particular, as mobile multimedia services are usually implemented using the UDP protocol, our architecture enables the transmission of each UDP datagram through the “most suitable” (e.g. most responsive, least loaded) NIC among those available at the time a datagram is transmitted. We term this operating mode of our architecture Always Best Packet Switching (ABPS). ABPS enables the use of policies for load balancing and recovery purposes. In essence, the architecture we propose consists of the following two principal components: (i) a fixed proxy server, which acts as a relay for the mobile node and enables communications from/to this node regardless of possible firewalls and NAT systems, and (ii) a proxy client running in the mobile node responsible for maintaining a multi-path tunnel, constructed out of all the node's NICs, with the above mentioned fixed proxy server. We show how the architecture supports multimedia applications based on the SIP and RTP/RTCP protocols, and avoids the typical delays introduced by the two way message/response handshake of the SIP signaling protocol. Experimental results originated from the implementation of a VoIP application on top of the architecture we propose show the effectiveness of our approach.     

Graphoid properties of epistemic irrelevance and independence

This paper investigates Walley's concepts of epistemic irrelevance and epistemic independence for imprecise probability models. We study the mathematical properties of irrelevance and independence, and their relation to the graphoid axioms. Examples are given to show that epistemic irrelevance can violate the symmetry, contraction and intersection axioms, that epistemic independence can violate contraction and intersection, and that this accords with informal notions of irrelevance and independence.     

From real to metaphoric maps: Cartography as a visual language for organizing and sharing knowledge

In this paper we discuss about geographic representations as a basis for describing, organizing, accessing and understanding heterogeneous shared information on the web. Maps are popular on the web, because of the reference to space, the most important domain of human experience, the proliferation of location aware devices and services and the availability of a set of tools that enable an heterogeneous population of users to explore and even modify these representations.Metaphorical maps, representing concepts and relations of a specific knowledge domain with symbols taken from another well known and widely used domain, couple the benefits of cartographic representation with the power and intuitiveness of the metaphor, permitting the communication and sharing of such knowledge.We introduce a classification of maps based on antinomies between real and imaginary worlds, and between direct and metaphoric knowledge; we argue that cartography can be used as a visual language for organizing and sharing knowledge related to different semantic domains, supporting our arguments with examples. Finally, we define a set of functions and related data structures able to support a user in browsing cartographic representations using state-of-art tools and systems available on the web.     

An efficient technique for implementing an image-compression neural algorithm on concurrent multiprocessor architectures

The paper describes a parallel implementation of a neural algorithm performing vector quantization for very low bit-rate video compression on toroidal-mesh multiprocessor systems. The neural model considered is a plastic version of the Neural Gas algorithm, whose features are suitable for implementations on toroidal mesh topologies. The architecture adopted, and the data-allocation strategy, enhance the method's scaling properties and remarkable efficiency. The parallel approach is supported by a theoretical analysis of the efficiency of the overall structure. Experimental results on a significant testbed and the fit between predicted and measured values confirm the validity of the parallel approach.     

Distributed estimation via iterative projections with application to power network monitoring

This work presents a distributed method for control centers to monitor the operating condition of a power network, i.e., to estimate the network state, and to ultimately determine the occurrence of threatening situations. State estimation has been recognized to be a fundamental task for network control centers to operate safely and reliably a power grid. We consider (static) state estimation problems, in which the state vector consists of the voltage magnitude and angle at all network buses. We consider the state to be linearly related to network measurements, which include power flows, current injections, and voltage phasors at some buses. We admit the presence of several cooperating control centers, and we design two distributed methods for them to compute the minimum variance estimate of the state, given the network measurements. The two distributed methods rely on different modes of cooperation among control centers: in the first method an incremental mode of cooperation is used, whereas, in the second method, a diffusive interaction is implemented. Our procedures, which require each control center to know only the measurements and the structure of a subpart of the whole network, are computationally efficient and scalable with respect to the network dimension, provided that the number of control centers also increases with the network cardinality. Additionally, a finite-memory approximation of our diffusive algorithm is proposed, and its accuracy is characterized. Finally, our estimation methods are exploited to develop a distributed algorithm to detect corrupted network measurements.