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Industry in all sectors is experiencing a profound digital transformation that puts software at the core of their businesses. In order to react to continuously changing user requirements and dynamic markets, companies need to build robust workflows that allow them to increase their agility in order to remain competitive. This increasingly rapid transformation, especially in domains like IoT or Cloud computing, poses significant challenges to guarantee high quality software, since dynamism and agile short-term planning reduce the ability to detect and manage risks. In this paper, we describe the main challenges related to managing risk in agile software development, building on the experience of more than 20 agile coaches operating continuously for 15 years with hundreds of teams in industries in all sectors. We also propose a framework to manage risks that considers those challenges and supports collaboration, agility, and continuous development. An implementation of that framework is then described in a tool that handles risks and mitigation actions associated with the development of multi-cloud applications. The methodology and the tool have been validated by a team of evaluators that were asked to consider its use in developing an urban smart mobility service and an airline flight scheduling system., 11 pages and 7 images (+1 for the logo). This paper is a postprint of a paper submitted to and accepted for publication in IET Software (Vol. 13 , Iss. 3 , 6 2019) and issubject to Institution of Engineering and Technology Copyright. The copy of record is available at the IET Digital Library

(a) Situation faced: In the era of pervasive digitalization, the airline IT software industry is facing a number of challenges from the combination of new distribution channels, social media, Big data, Cloud Computing, etc. One of the major challenges in creating smart and scalable software applications is how to tackle security challenges when components are distributed and operated in hybrid and multiple clouds, whose providers may be independent and heterogeneous. The difficulties reside not only in identifying and expressing the desired level of security in the application, but also in how the security guarantees are influenced by the cloud services used. (b) Action taken: We exemplify the case with a flight scheduling application prototype developed by Lufthansa Systems and explain how novel approaches are used to address security issues during the development of such a prototype by following the MUSA approach. MUSA stands for Multi-cloud Secure Applications and refers to an EU-funded research project that is developing an integrated solution for the development and operation of secure multi-cloud applications accounting for those security aspects from the beginning. We introduce the MUSA Security DevOps framework and lessons learned from using it. (c) Results achieved: Lufthansa Systems tested MUSA tools in an exercise to create, deploy and control a new secure application prototype. We describe how these tools were used in the context of the case study presented in this paper. We also analyze the impact that they had in the development, deployment, and operation of the multi-cloud prototype. This analysis is done by means of a user-centered evaluation using questionnaires and informal interviews. (d) Lessons learned: The most important lesson is the importance of a sound risk analysis from which the security decisions are taken. MUSA framework supports the automation of the risk analysis in a per component basis, helping to systematize the creation of the application risk profile. Another important aspect is how implementing a SecDevOps approach in a multi-cloud scenario proves that it is highly valuable to include security topics together with the regular DevOps methodology. Finally, we must underline the need for cloud standards which enable homogeneous cloud service descriptions that ease the comparison of the services and the offered security controls.

The use of Deep Learning (DL) algorithms is increasingly evolving in many application domains. Despite the rapid growing of algorithm size and complexity, performing DL inference at the edge is becoming a clear trend to cope with low latency, privacy and bandwidth constraints. Nevertheless, traditional implementation on low-energy computing nodes often requires experience-based manual intervention and trial-and-error iterations to get to a functional and effective solution. This work presents a computer-aided design (CAD) support for effective implementation of DL algorithms on embedded systems, aiming at automating different design steps and reducing cost. The proposed tool flow comprises capabilities to consider architecture-and hardware-related variables at very early stages of the development process, from pre-training hyperparameter optimization and algorithm configuration to deployment, and to adequately address security, power efficiency and adaptivity requirements. This paper also presents some preliminary results obtained by the first implementation of the optimization techniques supported by the tool flow.

The use of computational modeling studies accounts currently for the best approach to predict the electric field (E-field) distribution in transcranial direct current stimulation. As with any model, the values attributed to the physical properties, namely the electrical conductivity of the tissues, affect the predicted E-field distribution. A wide range of values for the conductivity of most tissues is reported in the literature. In this work, we used the finite element method to compute the E-field induced in a realistic human head model for two electrode montages targeting the left dorso-lateral prefrontal cortex (DLPFC). A systematic analysis of the effect of different isotropic conductivity profiles on the E-field distribution was performed for the standard bipolar 7×5 cm2 electrodes configuration and also for an optimized multielectrode montage. Average values of the E-field's magnitude, normal and tangential components were calculated in the target region in the left DLPFC. Results show that the field decreases with increasing scalp, cerebrospinal fluid (CSF) and grey matter (GM) conductivities, while the opposite is observed for the skull and white matter conductivities. The tissues whose conductivity most affects the E-field in the cortex are the scalp and the CSF, followed by the GM and the skull. Uncertainties in the conductivity of individual tissues may affect electric field values by up to about 80%.

This paper introduces a new 3D-based surveillance solution for large infrastructures. Our proposal is based on an accurate 3D reconstruction using the rich information obtained from a network of intelligent video-processing nodes. In this manner, if the scenario to cover is modeled in 3D with high precision, it will be possible to locate the detected objects in the virtual representation. Moreover, as an improvement over previous 2D solutions, having the possibility of modifying the view point enables the application to choose the perspective that better suits the current state of the scenario. In this sense, the contextualization of the events detected in a 3D environment can offer a much better understanding of what is happening in the real world and where it is exactly happening. Details of the video processing nodes are given, as well as of the 3D reconstruction tasks performed afterwards. The possibilities of such a system are described and the performance obtained is analyzed., This work has been partially supported by the ViCoMo project (ITEA2 project IP08009 funded by the Spanish MICINN with project TSI-020400-2011-57), the Spanish Government (TIN2009-14103-C03-03, DPI2008-06737-C02-01/02 and DPI 2011-28507-C02-02) and European FEDER funds.

Recently, multifocal transcranial current stimulation (tCS) devices using several relatively small electrodes have been used to achieve more focal stimulation of specific cortical targets. However, it is becoming increasingly recognized that many behavioral manifestations of neurological and psychiatric disease are not solely the result of abnormality in one isolated brain region but represent alterations in brain networks. In this paper we describe a method for optimizing the configuration of multifocal tCS for stimulation of brain networks, represented by spatially extended cortical targets. We show how, based on fMRI, PET, EEG or other data specifying a target map on the cortical surface for excitatory, inhibitory or neutral stimulation and a constraint on the maximal number of electrodes, a solution can be produced with the optimal currents and locations of the electrodes. The method described here relies on a fast calculation of multifocal tCS electric fields (including components normal and tangential to the cortical boundaries) using a five layer finite element model of a realistic head. Based on the hypothesis that the effects of current stimulation are to first order due to the interaction of electric fields with populations of elongated cortical neurons, it is argued that the optimization problem for tCS stimulation can be defined in terms of the component of the electric field normal to the cortical surface. Solutions are found using constrained least squares to optimize current intensities, while electrode number and their locations are selected using a genetic algorithm. For direct current tCS (tDCS) applications, we provide some examples of this technique using an available tCS system providing 8 small Ag/AgCl stimulation electrodes. We demonstrate the approach both for localized and spatially extended targets defined using rs-fcMRI and PET data, with clinical applications in stroke and depression. Finally, we extend these ideas to more general stimulation protocols, such as alternating current tCS (tACS).

Creation of 3D content in networked environments is a growing field of research, and its application will be key in many sectors such as medicine, simulation and cultural heritage. However, not many solutions exist that are able to use and manage 3D objects in such environments. In this paper, we present an architecture for the intelligent creation of imaginary worlds for running in a networked environment using technologies from disparate fields. This intelligent creation of an imaginary world enables end users to interactively run or walk within a 3D world. In order to improve the immersion experience we propose the use of external devices such as a treadmill and headphones with microphone. In this manner, users will interact with the system and with other users. The key components in the proposed architecture are a multiuser server, which will enable interaction among users, and a content server, which will piece together geospatial data and 3D media objects to automatically build the imaginary world and augment it with real data.

Current simulation applications are mainly focused on the efficient management of scenarios with static objects. However, managing dynamic objects, such as animated characters, is very different and requires more specific processing methods which tend to have a high computational cost. Recent advances in graphics hardware offer more ways to improve the performance of these scenes. In this paper, we introduce a new method for rendering large crowds of animated characters at interactive frame rates. Our method is a fully-GPU hybrid combination of mesh instancing, continuous level of detail and hardware palette skinning. Thus, we take advantage of mesh instancing to render multiple instances of a given mesh belonging to a continuous level of detail model, avoiding the typical popping artifacts existing on previous approaches. We finally obtained a low storage cost, performance improvements when applying level of detail and mesh instancing techniques and, moreover, a minimization of the overhead produced by animating.

Rain is a complex phenomenon and its simulation is usually very costly. In this article, the authors propose a fully-GPU rain simulation based on the utilization of particle systems. The flexibility of CUDA allows the authors to include, aside from the rainfall simulation, a system for the detection and handling of the collisions of particles against the scenario. This detection system allows for the simulation of splashes at the same time. This system obtains a very high performance because of the hardware programming capabilities of CUDA.

Virtual environments often include rain and other atmospheric phenomena which are difficult to simulate in real time. In the field of virtual reality a number of solutions have been proposed which offer realistic but costly rain systems. Our proposal consists in developing a solution to facilitate the creation of rain scenes and to reduce the cost of previous particle systems, while maintaining the realistic appearance of rain . This objective is accomplished by performing a suitable management of the particle system. We include level-of-detail techniques in order to adapt the number of particles, their location and their size according to the position of the observer and distance to the particles. The framework we propose is completely integrated within the GPU, thus offering a solution which is fast and efficient.