Informatica

Industry 4.0 solutions are composed of autonomous engineered systems where heterogeneous agents act in a choreographed manner to create complex workflows. Agents work at low-level in a flexible and independent manner, and their actions and behaviour may be sparsely manipulated. Besides, agents such as humans tend to show a very dynamic behaviour and processes may be executed in a very anarchic, but correct way. Thus, innovative, and more flexible control techniques are required. In this work, supervisory control techniques are employed to guarantee a correct execution of distributed and choreographed processes in Industry 4.0 scenarios. At prosumer level, processes are represented using soft models where logic rules and deformation indicators are used to analyse the correctness of executions. These logic rules are verified using specific engines at business level. These engines are fed with deformation metrics obtained through tensor deformation functions at production level. To apply deformation functions, processes are represented as discrete flexible solids in a phase space, under external forces representing the variations in every task’s inputs. The proposed solution presents two main novelties and original contributions. On the one hand, the innovative use of soft models and deformation indicators allows the implementation of this control solution not only in traditional industrial scenarios where rigid procedures are followed, but also in other future engineered applications. On the other hand, the original integration of logic rules and events makes possible to control any kind of device, including those which do not have an explicit control plane or interface. Finally, to evaluate the performance of the proposed solution, an experimental validation using a real pervasive computing infrastructure is carried out.

The paper deals with the causality perspective of the Enterprise Architecture (EA) frameworks. The analysis showed that there is a gap between the capabilities of EA frameworks and the behavioural characteristics of the real world domain (enterprise management activities). The contribution of research is bridging the gap between enterprise domain knowledge and EA framework content by the integration of meta-models as part of EA structures. Meta-models that cover not only simple process flows, but also business behaviour, i.e. causality of the domain, have been developed. Meta-models enable to create a layer of knowledge in the EA framework, which ensures smart EA development, allows validation of developer decisions. Two levels of the enterprise causal modelling were obtained. The first level uses the Management Transaction (MT) framework. At the second level, deep knowledge was revealed using a framework called the Elementary Management Cycle (EMC). These two causal frameworks were applied here to justify the causal meta-models of the EA. The new concepts Collapsed Capability, Capability Type and Capability Role which meaningfully complement MODAF with causal knowledge are introduced. Strategic Viewpoint (StV) modelling using causal meta-models is described in detail and illustrated in the case study. The example provided shows a principled way that causal knowledge supports the verification and validation of EA solutions. The presented method provides an opportunity to move the EA development to smart platforms.

In this work, we perform an extensive theoretical and experimental analysis of the characteristics of five of the most prominent algebraic modelling languages (AMPL, AIMMS, GAMS, JuMP, and Pyomo) and modelling systems supporting them. In our theoretical comparison, we evaluate how the reviewed modern algebraic modelling languages match the current requirements. In the experimental analysis, we use a purpose-built test model library to perform extensive benchmarks. We provide insights on which algebraic modelling languages performed the best and the features that we deem essential in the current mathematical optimization landscape. Finally, we highlight possible future research directions for this work.

(1) Background: Identifying early pancreas parenchymal changes remains a challenging radiologic diagnostic task. In this study, we hypothesized that applying artificial intelligence (AI) to contrast-enhanced ultrasound (CEUS) along with measurement of Heat Shock Protein (HSP)-70 levels could improve detection of early pancreatic necrosis in acute pancreatitis. (2) Methods: Acute pancreatitis $(n=146)$ and age- and sex matched healthy controls $(n=50)$ were enrolled in the study. The severity of acute pancreatitis was determined according to the revised Atlanta classification. The selected severe acute pancreatitis (AP) patient and an age/sex-matched healthy control were analysed for the algorithm initiation. Peripheral blood samples from the pancreatitis patient were collected on admission and HSP-70 levels were measured using ELISA. A CEUS device acquired multiple mechanical index contrast-specific mode images. Manual contour selection of the two-dimensional (2D) spatial region of interest (ROI) followed by calculations of the set of quantitative parameters. Image processing calculations and extraction of quantitative parameters from the CEUS diagnostic images were performed using algorithms implemented in the MATLAB software. (3) Results: Serum HSP-70 levels were 100.246 ng/ml (mean 76.4 ng/ml) at the time of the acute pancreatitis diagnosis. The CEUS Peek value was higher (155.5) and the mean transit time was longer (40.1 s) for healthy pancreas than in parenchyma affected by necrosis (46.5 and 34.6 s, respectively). (4) Conclusions: The extracted quantitative parameters and HSP-70 biochemical changes are suitable to be used further for AI-based classification of pancreas pathology cases and automatic estimation of pancreatic necrosis in AP.

Voting systems are as useful as people are willing to use them. Although many electronic election schemes have been proposed through the years, and some real case scenarios have been tested, people still do not trust electronic voting. Voting is not only about technological challenges but also about credibility, therefore, we propose a voting system focused on trust. We introduce political parties as active partners in the elections as a mechanism to encourage more traditional electors to participate. The system we propose here preserves elector’s privacy, it operates publicly through a blockchain and it is auditable by third parties.

It is a challenging task to prevent the staircase effect and simultaneously preserve sharp edges in image inpainting. For this purpose, we present a novel nonconvex extension model that closely incorporates the advantages of total generalized variation and edge-enhancing nonconvex penalties. This improvement contributes to achieve the more natural restoration that exhibits smooth transitions without penalizing fine details. To efficiently seek the optimal solution of the resulting variational model, we develop a fast primal-dual method by combining the iteratively reweighted algorithm. Several experimental results, with respect to visual effects and restoration accuracy, show the excellent image inpainting performance of our proposed strategy over the existing powerful competitors.

In the family of Intelligent Transportation Systems (ITS), Multimodal Transport Systems (MMTS) have placed themselves as a mainstream transportation mean of our time as a feasible integrative transportation process. The Global Economy progressed with the help of transportation. The volume of goods and distances covered have doubled in the last ten years, so there is a high demand of an optimized transportation, fast but with low costs, saving resources but also safe, with low or zero emissions. Thus, it is important to have an overview of existing research in this field, to know what has already been done and what is to be studied next. The main objective is to explore a beneficent selection of the existing research, methods and information in the field of multimodal transportation research, to identify industry needs and research gaps and provide context for future research. The selective survey covers multimodal transport design and optimization in terms of: cost, time, and network topology. The multimodal transport theoretical aspects, context and resources are also covering various aspects. The survey‘s selection includes currently existing best methods and solvers for Intelligent Transportation Systems (ITS). The gap between theory and real-world applications should be further solved in order to optimize the global multimodal transportation system.

Blockchain is a decentralized database, which can protect the safety of trade and avoid double payment. Due to the widespread attention of researchers, the studies of this field have increased sharply in recent years. It is meaningful to reveal the development level and trends based on this literature. This paper adopts bibliometric methods to study the collaboration characteristics from the levels of author, institution and country. Furthermore, several kinds of collaboration networks and their centrality analysis are also presented, which not only display the development level and collaboration degree but also the evolution of author collaboration modes in different phases.

In this work, we propose a novel framework based on Generative Adversarial Networks for pose face augmentation (PFA-GAN). It enables a controlled pose synthesis of a new face image from a source face given a driving one while preserving the identity of the source face. We introduce a method for training the framework in a fully self-supervised mode using a large-scale dataset of unconstrained face images. Besides, some augmentation strategies are presented to expand the training set. The face verification experimental results demonstrate the effectiveness of the presented augmentation strategies as all augmented datasets outperform the baseline.