IMP Science Gateway: from the Portal to the Hub of Virtual Experimental Labs in e‐Science and Multiscale Courses in e‐Learning

-The concept of the augmented coaching ecosystem for non-obtrusive adaptive personalized elderly care is proposed on the basis of the integration of new and available ICT approaches. They include multimodal user interface (MMUI), augmented reality (AR), machine learning (ML), Internet of Things (IoT), and machine-tomachine (M2M) interactions. The ecosystem is based on the Cloud-Fog-Dew computing paradigm services, providing a full symbiosis by integrating the whole range from low level sensors up to high level services using integration efficiency inherent in synergistic use of applied technologies. Inside of this ecosystem, all of them are encapsulated in the following network layers: Dew, Fog, and Cloud computing layer. Instead of the "spaghetti connections", "mosaic of buttons", "puzzles of output data", etc., the proposed ecosystem provides the strict division in the following dataflow channels: consumer interaction channel, machine interaction channel, and caregiver interaction channel. This concept allows to decrease the physical, cognitive, and mental load on elderly care stakeholders by decreasing the secondary human-to-human (H2H), human-to-machine (H2M), and machine-to-human (M2H) interactions in favor of M2M interactions and distributed Dew Computing services environment. It allows to apply this non-obtrusive augmented reality ecosystem for effective personalized elderly care to preserve their physical, cognitive, mental and social well-being.

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“…• Frameworks for Integration of Workflows and Distributed Computing Resources: gateway approaches in science and education [16][17][18];…”

Section: Some Implemented Combinations Of Componentsmentioning

confidence: 99%

Augmented Coaching Ecosystem for Non-obtrusive Adaptive Personalized Elderly Care on the basis of Cloud-Fog-Dew computing paradigm

Gordienko

Stirenko

Alienin

et al. 2017

2017 40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)

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“…The examples of their implementations (like WS-PGRADE/gUSE [9], KNIME [10], etc.) and applications in physics [11], chemistry [12], astronomy [13], brain-computing [14], eHealth [15] can be found elsewhere. In this context TensorFlow and H2O propose web-based graphic user interfaces Tensor-Board and Flow, respectively, which are actually workflow management and visualization tools.…”

Section: Comparative Analysismentioning

confidence: 99%

Performance Analysis of Open Source Machine Learning Frameworks for Various Parameters in Single-Threaded and Multi-threaded Modes

Kochura¹,

Stirenko²,

Alienin³

et al. 2017

Advances in Intelligent Systems and Computing II

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The basic features of some of the most versatile and popular open source frameworks for machine learning (TensorFlow, Deep Learning4j, and H2O) are considered and compared. Their comparative analysis was performed and conclusions were made as to the advantages and disadvantages of these platforms. The performance tests for the de facto standard MNIST data set were carried out on H2O framework for deep learning algorithms designed for CPU and GPU platforms for single-threaded and multithreaded modes of operation Also, we present the results of testingneural networks architectures on H2O platform for variousactivation functions, stopping metrics, and other parameters ofmachine learning algorithm. It was demonstrated for the usecase of MNIST database of handwritten digits in single-threadedmode that blind selection of these parameters can hugely increase (by 2-3 orders) the runtime without the significant increase ofprecision. This result can have crucial influence for optimizationof available and new machine learning methods, especially forimage recognition problems.

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“…The examples of their implementations (like WS-PGRADE/gUSE [5], KNIME [6], etc.) and applications in physics [7], chemistry [8], astronomy [9], brain-computing [10], eHealth [11] can be found elsewhere. In this context TensorFlow and H2O propose web-based graphic user interfaces TensorBoard and Flow, respectively, which are actually workflow management and visualization tools.…”

Section: Comparative Analysismentioning

confidence: 99%

Comparative analysis of open source frameworks for machine learning with use case in single-threaded and multi-threaded modes

Kochura¹,

Stirenko²,

Rojbi³

et al. 2017

2017 12th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT)

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IMP Science Gateway: from the Portal to the Hub of Virtual Experimental Labs in e‐Science and Multiscale Courses in e‐Learning

Cited by 9 publications

References 31 publications

Augmented Coaching Ecosystem for Non-obtrusive Adaptive Personalized Elderly Care on the basis of Cloud-Fog-Dew computing paradigm

Augmented Coaching Ecosystem for Non-obtrusive Adaptive Personalized Elderly Care on the basis of Cloud-Fog-Dew computing paradigm

Performance Analysis of Open Source Machine Learning Frameworks for Various Parameters in Single-Threaded and Multi-threaded Modes

Comparative analysis of open source frameworks for machine learning with use case in single-threaded and multi-threaded modes

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