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  • 1
    Electronic Resource
    Electronic Resource
    Springer
    Journal of intelligent and robotic systems 26 (1999), S. 123-135 
    ISSN: 1573-0409
    Keywords: named-entity recognition ; information extraction ; machine learning
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract Named-entity recognition (NER) involves the identification and classification of named entities in text. This is an important subtask in most language engineering applications, in particular information extraction, where different types of named entity are associated with specific roles in events. In this paper, we present a prototype NER system for Greek texts that we developed based on a NER system for English. Both systems are evaluated on corpora of the same domain and of similar size. The time-consuming process for the construction and update of domain-specific resources in both systems led us to examine a machine learning method for the automatic construction of such resources for a particular application in a specific language.
    Type of Medium: Electronic Resource
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  • 2
    Publication Date: 2018-12-13
    Description: In this article, we present a taxonomy in Robot-Assisted Training; a growing body of research in Human–Robot Interaction which focuses on how robotic agents and devices can be used to enhance user’s performance during a cognitive or physical training task. Robot-Assisted Training systems have been successfully deployed to enhance the effects of a training session in various contexts, i.e., rehabilitation systems, educational environments, vocational settings, etc. The proposed taxonomy suggests a set of categories and parameters that can be used to characterize such systems, considering the current research trends and needs for the design, development and evaluation of Robot-Assisted Training systems. To this end, we review recent works and applications in Robot-Assisted Training systems, as well as related taxonomies in Human–Robot Interaction. The goal is to identify and discuss open challenges, highlighting the different aspects of a Robot-Assisted Training system, considering both robot perception and behavior control.
    Electronic ISSN: 2227-7080
    Topics: Technology
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  • 3
    Publication Date: 2019-04-25
    Description: Evidence transfer for clustering is a deep learning method that manipulates the latent representations of an autoencoder according to external categorical evidence with the effect of improving a clustering outcome. Evidence transfer’s application on clustering is designed to be robust when introduced with a low quality of evidence, while increasing the effectiveness of the clustering accuracy during relevant corresponding evidence. We interpret the effects of evidence transfer on the latent representation of an autoencoder by comparing our method to the information bottleneck method. Information bottleneck is an optimisation problem of finding the best tradeoff between maximising the mutual information of data representations and a task outcome while at the same time being effective in compressing the original data source. We posit that the evidence transfer method has essentially the same objective regarding the latent representations produced by an autoencoder. We verify our hypothesis using information theoretic metrics from feature selection in order to perform an empirical analysis over the information that is carried through the bottleneck of the latent space. We use the relevance metric to compare the overall mutual information between the latent representations and the ground truth labels before and after their incremental manipulation, as well as, to study the effects of evidence transfer regarding the significance of each latent feature.
    Electronic ISSN: 2227-9709
    Topics: Computer Science
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  • 4
    Publication Date: 2021-01-11
    Description: The DARE e-science platform (http://project-dare.eu) offers innovative tools to ease scientific workflow development and execution exploiting efficient Cloud resources. It aims to enable on-demand numerical computations and analyses, fast large dataset handling, flexible and customisable workflow pipelines and complete provenance tracking. It also integrates available e-infrastructure services (e.g. EUDAT, EIDA) and can be linked to user developed interfaces. DARE is validated via two domain-specific pilots, one from the climate modelling community and one from the seismological research field. Focusing on the latter, the EPOS Use Case is driven by urgent issues and general user needs of solid Earth Science community, following developments and application standards in the computational seismology research society. This Use Case also benefits from the pioneering experience of previous European projects (e.g. VERCE, EPOS-IP) in this framework. We present here the development of a scientific workflow to perform a quick calculation of seismic source parameters after an earthquake. The workflow requirements include HPC calculations (on local-institutional or Cloud resources), fast data-intensive processing, provenance exploitation and seismic source inverse modelling tools. The DARE platform automatically conducts the required actions optimally mapped to computational resources, linking them together by managing intermediate data. It automatically deploys the necessary environment to perform on-demand transparent computations executing a dockerised version of the numerical simulation code on a Kubernetes cluster via a web API. Other API calls allow for remote, distributed execution of dispel4py workflows, used to describe the steps for data analysis and download of seismic recorded data via EIDA Research Infrastructure services. Well established scientific python codes, such as those for waveform misfit calculation and source inversion, are thus easily implemented in this flexible and modular structure, and executed at scale. Moreover, the pilot requirement of searching and reusing multiple simulations for the same earthquake strongly benefits from customisable management of metadata and lineage through the DARE platform exploiting the integration of S-ProvFlow with dispel4py.
    Description: Published
    Description: San Francisco, CA, USA
    Description: 3IT. Calcolo scientifico
    Keywords: e-Science platform ; scientific workflow ; provenance ; metadata ; cloud ; EPOS ; numerical simulations ; dispel4py
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: Poster session
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  • 5
    Publication Date: 2021-05-12
    Description: UEDIN, INGV, SCAI, KNMI, NCSR-D, KIT, GRNET
    Description: Published
    Description: 3IT. Calcolo scientifico
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: report
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  • 6
    Publication Date: 2023-01-30
    Description: The DARE platform has been designed to help research developers deliver user-facing applications and solutions over diverse underlying e-infrastructures, data and computational contexts. The platform is Cloud-ready, and relies on the exposure of APIs, which are suitable for raising the abstraction level and hiding complexity. At its core, the platform implements the cataloguing and execution of fine-grained and Python-based dispel4py workflows as services. Reflection is achieved via a logical knowledge base, comprising multiple internal catalogues, registries and semantics, while it supports persistent and pervasive data provenance. This paper presents design and implementation aspects of the DARE platform, as well as it provides directions for future development.
    Description: Published
    Description: San Diego (CA, USA)
    Description: 3IT. Calcolo scientifico
    Keywords: software platform ; cloud ; technology ; conceptualization ; data-driven science ; scientific workflows ; provenance ; workflow optimization
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: Conference paper
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