Abstract
Devido ao ambiente hipercompetitivo que as empresas industriais enfrentam, a adequada implementação de Meta-Organizações, como plataformas organizacionais que promovam o desenvolvimento de modelos avançados de sistemas de produção em rede, virtuais e ubíquas, torna-se fundamental para a sustentabilidade da Meta-Organização e das empresas industriais que a constituem. A estratégia de implementação encontra-se relacionada com o entendimento que as empresas industriais, no caso específico deste estudo, as Pequenas e Médias Empresas (PMEs) industriais de clusters regionais, têm acerca deste tipo de Meta-Organização. Este estudo tem como objetivo validar as principais hipóteses: 1) as empresas da região estão conscientes da necessidade da adoção de Meta-Organizações como plataformas organizacionais para o desenvolvimento de modelos de Empresas Virtuais e Ubíquas; e 2) as empresas da região estão aptas para a utilização de Meta-Organizações no apoio ao desenvolvimento de modelos de Empresas Virtuais e Ubíquas. Para validação destas teses foram desenvolvidos modelos teóricos originais utilizando o método estatístico de Análise de Equações Estruturais constituídos por oito constructos: “Prática”, “Gestão do Conhecimento”, “Característica da PME Industrial”, “Conhecimento”, “Necessidade de Estruturas de Meta-Organização para Empresas Virtuais e Ubíquas”, “Necessidade para a Gestão da Meta-Organização para Empresas Virtuais e Ubíquas”, “Consciência” e “Aptidão”. A recolha de dados foi realizada através da implementação de um inquérito original em que participaram 236 PMEs (industriais ou que trabalham com a indústria) da região da Península Ibérica. Os resultados obtidos validam as principais hipóteses sobre a consciência e a aptidão das empresas e fornecem outros resultados derivados de outras hipóteses inerentes aos modelos desenvolvidos. O presente estudo contribui para definir os mecanismos de escolha da implementação de Meta- Organizações que apoiam o desenvolvimento de modelos de Empresas Virtuais e Ubíquas.;Due to the hypercompetitive environment that industrial enterprises face, a proper implementation of the Meta-Organizations, as organizational platforms, that promote the development of advanced models of networked, virtual and ubiquitous production systems, becomes fundamental for the sustainability of the Meta-Organization and of the industrial enterprises that belong to it. The implementation strategy is related to the understanding that the industrial enterprises, in the specific case of this study, the Industrial Small and Medium-sized Enterprises (SMEs) of regional clusters, have about this type of Meta-Organization. This study aims to validate the following main hypotheses: 1) enterprises in the region are aware of the need to adopt Meta-Organizations as an organizational platform for the development of Virtual and Ubiquitous Enterprises models; and 2) enterprises in the region are prepared to use Meta- Organizations to support the development of Virtual and Ubiquitous Enterprises models. To validate these theses, original theoretical models were developed using the statistical method of Structural Equation Modeling constituted by eight constructs: “Practice”, “Knowledge Management”, “Characteristics of Industrial SME”, “Knowledge”, “Need for Meta-Organization Structures for Virtual and Ubiquitous Enterprises”, “Need for Meta-Organization Management for Virtual and Ubiquitous Enterprises”, “Awareness”, and “Preparedness”. Data collection was carried out through the implementation of an original survey in which 236 SMEs (industrial or those working with the industry) in the Iberian Peninsula region participated. The results validate the main hypotheses concerning enterprises’ awareness and preparedness, and provide further results derived from other hypotheses inherent to the developed models. The present study contributes to define the mechanisms of choice for the implementation of Meta- Organizations that support the development of Virtual and Ubiquitous Enterprises models.

Abstract
Automatic parallelization of sequential code has become increasingly relevant in multicore programming. In particular, loop parallelization continues to be a promising optimization technique for scienti.c applications, and can provide considerable speedups for program execution. Furthermore, if we can verify that there are no true data dependencies between loop iterations, they can be easily parallelized. This paper describes Clava AutoPar, a library for the Clava weaver that performs automatic and symbolic parallelization of C code. The library is composed of two main parts, parallel loop detection and source-to-source code parallelization. The system is entirely automatic and attempts to statically detect parallel loops for a given input program, without any user intervention or profiling information. We obtained a geometric mean speedup of 1.5 for a set of programs from the C version of the NAS benchmark, and experimental results suggest that the performance obtained with Clava AutoPar is comparable or better than other similar research and commercial tools.

Abstract
This paper presents the concept of a tapered deep neural network, subject to unsupervised training layer by layer, under a criterion of maximum entropy, to perform the estimation of breaker status in the absence of a specific sensor signal. The almost perfect prediction power of the model confirms the conjecture that the knowledge of the topology of a network is hidden in the electric measurement values in the network. A test case is presented with computing speed accelerated by using a GPU (graphics processing unit). The comparison with a previous model illustrates the superiority of the novel approach. © 2018 IEEE.

Abstract
Imbalanced classification has been extensively researched in the last years due to its prevalence in real-world datasets, ranging from very different topics such as health care or fraud detection. This literature has long been dominated by variations of the same family of solutions (e.g. mainly resampling and cost-sensitive learning). Recently, a new and promising way of tackling this problem has been introduced: learning with scoring pairwise ranking so that each pair of classes contribute in tandem to the decision boundary. In this sense, the paper addresses the problem of class imbalance in the context of ordinal regression, proposing two novel contributions: (a) approaching the imbalance by binary pairwise ranking using a well-known label decomposition ensemble, and (b) introducing a regularization into this ensemble so that parallel decision boundaries are favored. These are two independent contributions that synergize well. Our model is tested using linear Support Vector Machines and our results are compared against state-of-the-art models. Both approaches show promising performance in ordinal class imbalance, with an overall 15% improvement relative to the state-of-the-art, as evaluated by a balanced metric.

Abstract
FLUX is a platform for dynamically reconfigurable crowd-sensing using mobile devices like smartphones and tablets, programmed under a notion of region-based sensing. Each region is defined by a set of physical constraints that determine the sensing scope, e.g., based on device position or other environmental variables, plus a set of periodic tasks that perform the actual sensing. The resulting behavior is inherently dynamic: as a device's state changes, e.g., moves in space, it enters and/or leaves different regions, thereby changing the set of active tasks; moreover, regions can be added, deleted, and reprogrammed on-the-fly. FLUX makes use of a domain-specific language for sensing tasks that is compiled into abstract bytecode, later executed by a low-footprint virtual machine within a device, guaranteeing runtime safety by construction. For region/task dissemination, FLUX employs a broker that holds a changeable region configuration plus gateways that mirror the configuration throughout different network access points to which devices connect. Sensing data is streamed by devices to gateways and then back to the broker. Live or archived data streams are in turn fed by the broker to data-processing clients, which interface with the broker using a publish/subscribe API. We conducted two case-study experiments illustrating FLUX: a single-region deployment to monitor WiFi signal quality, and a multi-region deployment to monitor noise, temperature, and places-of-interest based on device movement.

Abstract
Considering an environment that consists of several services, applications and platforms, each present entity produces a certain amount of data. With so many sources of data, there are a number of things bound to exist: different formats of information, redundancy and no consistent standards of information. In environments as these, the collaboration between different entities creates an opportunity for innovation, where data interoperability allows for the re-use of information, the possibility of different services taking advantage of other third-party sources and the development of new businesses from existing information. This, however, is only possible if there is some sort of interoperability between the data, a way for it to be transmitted from entity to entity, always with the possibility of creating value with its manipulation and consumption. This paper exposes the work done in the development of a platform focused on data, looking into its forms of representation and how to solve the problems caused by the ever existing necessity of data interoperability between systems. The possibility for maintaining and creating Open Data Ecosystems is also analysed in the scope of the proposed platform. © 2018 IEEE.