67,139 research outputs found

    Low-Code/No-Code Artificial Intelligence Platforms for the Health Informatics Domain

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    In the contemporary health informatics space, Artificial Intelligence (AI) has become a necessity for the extraction of actionable knowledge in a timely manner. Low-code/No-Code (LCNC) AI Platforms enable domain experts to leverage the value that AI has to offer by lowering the technical skills overhead. We develop domain-specific, service-orientated platforms in the context of two subdomains of health informatics. We address in this work the core principles and the architectures of these platforms whose functionality we are constantly extending. Our work conforms to best practices with respect to the integration and interoperability of external services and provides process orchestration in a LCNC modeldriven fashion. We chose the CINCO product DIME and a bespoke tool developed in CINCO Cloud to serve as the underlying infrastructure for our LCNC platforms which address the requirements from our two application domains; public health and biomedical research. In the context of public health, an environment for building AI driven web applications for the automated evaluation of Web-based Health Information (WBHI). With respect to biomedical research, an AI driven workflow environment for the computational analysis of highly-plexed tissue images. We extended both underlying application stacks to support the various AI service functionality needed to address the requirements of the two application domains. The two case studies presented outline the methodology of developing these platforms through co-design with experts in the respective domains. Moving forward we anticipate we will increasingly re-use components which will reduce the development overhead for extending our existing platforms or developing new applications in similar domains

    Automated migration of EuGENia graphical editors to the web

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    © ACM 2020. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings, http://dx.doi.org/10.1145/10.1145/3417990.3420205Domain-specific languages (DSLs) are languages tailored for particular domains. Many frameworks and tools have been proposed to develop editors for DSLs, especially for desktop IDEs, like Eclipse. We are witnessing the advent of low-code development platforms, which are cloud-based environments supporting rapid application development by using graphical languages and forms. While this approach is very promising, the creation of new low-code platforms may require the migration of existing desktop-based editors to the web. However, this is a technically challenging task. To fill this gap, we present ROCCO, a tool that migrates Eclipse-based graphical modelling editors to the web, to facilitate their integration with low-code platforms. The tool reads a meta-model annotated with EuGENia annotations, and generates a web editor using the DPG web framework used by the UGROUND company. In this paper, we present the approach, including tool support and an evaluation based on migrating nine editors created by third parties, which shows the usefulness of the toolThis project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement n° 813884, Lowcomote [33]. The work has also been supported by the Spanish Ministry of Science (project MASSIVE, RTI2018-095255-B-I00) and the R&D programme of Madrid (project FORTE, P2018/TCS-4314

    Online citizen reporting on urban maintenance: a collection, evaluation and decision support system

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    We present an online support system for urban maintenance which: 1. lets citizens directly report neighbourhood issues which may require attention from the urban maintenance services: 2. evaluates the priority of reported issues; 3. allows the allocation and management of resources and workforce on solving issues and 4. permits public tracking of their status. The web application was entirely developed using low-cost Google cloud services, with the advantage of low deployment and hosting costs and practically no systems administration costs, a highly replicable and transferrable solution, and a rapid development process relying on robust Google services. The model for evaluating priority of reported issues is based on the the ELECTRE TRI rating method. In the paper we present the system's standard workflow, the evaluation model and the implementation details. We also discuss its possible more general implications for fostering and supporting citizens participation. Unlike many existing platforms for citizens reporting of maintenance issues, our system incorporates an explicit and publicly accessible evaluation model to prioritise issues and assign resources for their solution. This, we argue, is a crucial prerequisite for the principles of transparency, publicity, accountability and equity be observed by municipal governments

    Transparent Orchestration of Task-based Parallel Applications in Containers Platforms

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    This paper presents a framework to easily build and execute parallel applications in container-based distributed computing platforms in a user-transparent way. The proposed framework is a combination of the COMP Superscalar (COMPSs) programming model and runtime, which provides a straightforward way to develop task-based parallel applications from sequential codes, and containers management platforms that ease the deployment of applications in computing environments (as Docker, Mesos or Singularity). This framework provides scientists and developers with an easy way to implement parallel distributed applications and deploy them in a one-click fashion. We have built a prototype which integrates COMPSs with different containers engines in different scenarios: i) a Docker cluster, ii) a Mesos cluster, and iii) Singularity in an HPC cluster. We have evaluated the overhead in the building phase, deployment and execution of two benchmark applications compared to a Cloud testbed based on KVM and OpenStack and to the usage of bare metal nodes. We have observed an important gain in comparison to cloud environments during the building and deployment phases. This enables better adaptation of resources with respect to the computational load. In contrast, we detected an extra overhead during the execution, which is mainly due to the multi-host Docker networking.This work is partly supported by the Spanish Government through Programa Severo Ochoa (SEV-2015-0493), by the Spanish Ministry of Science and Technology through TIN2015-65316 project, by the Generalitat de Catalunya under contracts 2014-SGR-1051 and 2014-SGR-1272, and by the European Union through the Horizon 2020 research and innovation program under grant 690116 (EUBra-BIGSEA Project). Results presented in this paper were obtained using the Chameleon testbed supported by the National Science Foundation.Peer ReviewedPostprint (author's final draft

    A gap analysis of Internet-of-Things platforms

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    We are experiencing an abundance of Internet-of-Things (IoT) middleware solutions that provide connectivity for sensors and actuators to the Internet. To gain a widespread adoption, these middleware solutions, referred to as platforms, have to meet the expectations of different players in the IoT ecosystem, including device providers, application developers, and end-users, among others. In this article, we evaluate a representative sample of these platforms, both proprietary and open-source, on the basis of their ability to meet the expectations of different IoT users. The evaluation is thus more focused on how ready and usable these platforms are for IoT ecosystem players, rather than on the peculiarities of the underlying technological layers. The evaluation is carried out as a gap analysis of the current IoT landscape with respect to (i) the support for heterogeneous sensing and actuating technologies, (ii) the data ownership and its implications for security and privacy, (iii) data processing and data sharing capabilities, (iv) the support offered to application developers, (v) the completeness of an IoT ecosystem, and (vi) the availability of dedicated IoT marketplaces. The gap analysis aims to highlight the deficiencies of today's solutions to improve their integration to tomorrow's ecosystems. In order to strengthen the finding of our analysis, we conducted a survey among the partners of the Finnish IoT program, counting over 350 experts, to evaluate the most critical issues for the development of future IoT platforms. Based on the results of our analysis and our survey, we conclude this article with a list of recommendations for extending these IoT platforms in order to fill in the gaps.Comment: 15 pages, 4 figures, 3 tables, Accepted for publication in Computer Communications, special issue on the Internet of Things: Research challenges and solution

    Arm Mbed – AWS IoT System Integration [Open access]

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    This project explores the different Internet of Things (IoT) architectures and the available platforms to define a general IoT Architecture to connect Arm microcontrollers to Amazon Web Services. In order to accommodate the wide range of IoT applications, the architecture was defined with different routes that an Arm microcontroller can take to reach AWS. Once this Architecture was defined, a performance analysis on the different routes was performed in terms of communication speed and bandwidth. Finally, a Smart Home use case scenario is implemented to show the basic functionalities of an IoT system such as sending data to the device and data storage in the Cloud. Furthermore, a Cloud ML algorithm is triggered in real time by the Smart Home to receive a prediction of the current Comfort Level in the room
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