The data retrieved from satellite imagery and ground-based photometers are the two main sources of information on light pollution and are thus the two main tools for tackling the problem of ...artificial light pollution at night (ALAN). While satellite data offer high spatial coverage, on the other hand, photometric data provide information with a higher degree of temporal resolution. Thus, studying the proper correlation between both sources will allow us to calibrate and integrate them to obtain data with both high temporal resolution and spatial coverage. For this purpose, more than 15,000 satellite measurements and 400,000 measurements from 72 photometers for the year 2022 were used. The photometers used were the Sky-Glow Wireless Autonomous Sensor (SG-WAS) and Telescope Encoder and Sky Sensor WIFI (TESS-W) types, located at different ground-based locations, mainly in Spain. These photometers have a spectral sensitivity closer to that of VIIRS than to the Sky Quality Meter (SQM). In this study, a good correlation of data from the Day–Night Band (DNB) from the Visible Infrared Imaging Radiometer Suite (VIIRS) with a red photometric network between 19.41 mag/arcsec2 and 21.12 mag/arcsec2 was obtained.
Data scarcity is a common and challenging issue when working with Artificial Intelligence solutions, especially those including Deep Learning (DL) models for tasks such as image classification. This ...is particularly relevant in healthcare scenarios, in which data collection requires a long-lasting process, involving specific control protocols. The performance of DL models is usually quantified by different classification metrics, which may provide biased results, due to the lack of sufficient data. In this paper, an innovative approach is proposed to evaluate the performance of DL models when labeled data is scarce. This approach, which aims to detect the poor performance provided by DL models, in spite of traditional assessing metrics indicating otherwise, is based on information theoretic concepts and motivated by the Information Bottleneck framework. This methodology has been evaluated by implementing several experimental configurations to classify samples from a plantar thermogram dataset, focused on early stage detection of diabetic foot ulcers, as a case study. The proposed network architectures exhibited high results in terms of classification metrics. However, as our approach shows, only two of those models are indeed consistent to generalize the data properly. In conclusion, a new methodology was introduced and tested to identify promising DL models for image classification over small datasets without relying exclusively on the widely employed classification metrics. Example code and supplementary material using a state-of-the-art DL model are available at https://github.com/mt4sd/PerformanceEvaluationScarceDataset .
Due to the increasing uptake of semantic technologies, ontologies are now part of a good number of information systems. As a result, software development teams that have to combine ontology ...engineering activities with software development practices are facing several challenges, since these two areas have evolved, in general, separately. In this paper we present OnToology, an approach to manage ontology engineering support activities (i.e., documentation, evaluation, releasing and versioning). OnToology is a web-based application that builds on top of Git-based environments and integrates existing semantic web technologies. We have validated OnToology against a set of representative requirements for ontology development support activities in distributed environments, and report on a survey of the system to assess its usefulness and usability.
In the past decades, one of the most common forms of addressing reproducibility in scientific workflow-based computational science has consisted of tracking the provenance of the produced and ...published results. Such provenance allows inspecting intermediate and final results, improves understanding, and permits replaying a workflow execution. Nevertheless, this approach does not provide any means for capturing and sharing the very valuable knowledge about the experimental equipment of a computational experiment, i.e., the execution environment in which the experiments are conducted. In this work, we propose a novel approach based on semantic vocabularies that describes the execution environment of scientific workflows, so as to conserve it. We define a process for documenting the workflow application and its related management system, as well as their dependencies. Then we apply this approach over three different real workflow applications running in three distinct scenarios, using public, private, and local Cloud platforms. In particular, we study one astronomy workflow and two life science workflows for genomic information analysis. Experimental results show that our approach can reproduce an equivalent execution environment of a predefined virtual machine image on all evaluated computing platforms.
•A semantic modeling approach to conserve computational environments of scientific workflow executions.•A process for describing scientific workflow applications, a workflow management system and its dependencies.•Two life-science applications and one astronomy application are documented and reproduced.
It is commonly agreed that in silico scientific experiments should be executable and repeatable processes. Most of the current approaches for computational experiment conservation and reproducibility ...have focused so far on two of the main components of the experiment, namely, data and method. In this paper, we propose a new approach that addresses the third cornerstone of experimental reproducibility: the equipment. This work focuses on the equipment of a computational experiment, that is, the set of software and hardware components that are involved in the execution of a scientific workflow. In order to demonstrate the feasibility of our proposal, we describe a use case scenario on the Text Analytics domain and the application of our approach to it. From the original workflow, we document its execution environment, by means of a set of semantic models and a catalogue of resources, and generate an equivalent infrastructure for reexecuting it.
OLC, On-Line Compiler to Teach Programming Languages Cayetano Guerra Artal; Afonso Suarez, Maria Dolores; Idafen Santana Perez ...
International journal of computers, communications & control,
03/2008, Letnik:
3, Številka:
1
Journal Article
Recenzirano
Odprti dostop
The advance of Internet towards Web 2.0 conveys the potential it has in a wide range of scopes. The ongoing progress of the Web technology and its availability in teaching and learning, as well as a ...students’ profile increasingly more used to managing an important amount of digital information, offers lecturers the opportunity and challenge of putting at students’ disposal didactic tools making use of the Internet. Programming is one of the essential areas taught in university studies of Computer Science and other engineering degrees. At present, it is a knowledge acquired through tutorial classes and the practice with different tools for programming. This paper shows the acquired experience in the development and use of a simple compiler accessible through a Web page. In addition it presents a teaching proposal for its use in subjects that include programming languages lessons. OLC - On-Line Compiler - is an application which greatly lightens the student’s workload at the initial stage of programming. During this initial period they will neither have to deal with the complexities of the installation and the configuration of these types of tools, nor with the understanding of multiple options which they present. Therefore students can concentrate on the comprehension of the programming structures and the programming language to be studied.
This paper presents a disambiguation method that diminishes the functional combinations of the words of a sentence taking into account the context in which they appear. This process uses an algorithm ...which does the syntactic analysis of every functional combination of the sentece. In order to control this analysis, a grammar with restrictions has been developed to model the valid syntactic structures of the Spanish language. The main target of our algorithm is the separation between the disambiguation method and the grammar which governs it.
Extension of the BiDO Ontology to Represent Scientific Production Tapia-Leon, Mariela; Santana-Perez, Idafen; Poveda-Villalón, María ...
Proceedings of the 2019 8th International Conference on Educational and Information Technology,
03/2019
Conference Proceeding
The SPAR Ontology Network is a suite of complementary ontology modules to describe the scholarly publishing domain. BiDO Standard Bibliometric Measures is part of its set of ontologies. It allows ...describing of numerical and categorical bibliometric data such as h-index, author citation count, journal impact factor. These measures may be used to evaluate scientific production of researchers. However, they are not enough. In a previous study, we determined the lack of some terms to provide a more complete representation of scientific production. Hence, we have built an extension using the NeOn Methodology to restructure the BiDO ontology. With this extension, it is possible to represent and measure the number of documents from research, the number of citations from a paper and the number of publications in high impact journals according to its area and discipline.
Data scarcity is a common and challenging issue when working with Artificial Intelligence solutions, especially those including Deep Learning (DL) models for tasks such as image classification. This ...is particularly relevant in healthcare scenarios, in which data collection requires a long-lasting process, involving specific control protocols. The performance of DL models is usually quantified by different classification metrics, which may provide biased results, due to the lack of sufficient data. In this paper, an innovative approach is proposed to evaluate the performance of DL models when labeled data is scarce. This approach, which aims to detect the poor performance provided by DL models, in spite of traditional assessing metrics indicating otherwise, is based on information theoretic concepts and motivated by the Information Bottleneck framework. This methodology has been evaluated by implementing several experimental configurations to classify samples from a plantar thermogram dataset, focused on early stage detection of diabetic foot ulcers, as a case study. The proposed network architectures exhibited high results in terms of classification metrics. However, as our approach shows, only two of those models are indeed consistent to generalize the data properly. In conclusion, a new methodology was introduced and tested to identify promising DL models for image classification over small datasets without relying exclusively on the widely employed classification metrics. Example code and supplementary material using a state-of-the-art DL model are available at https://github.com/mt4sd/PerformanceEvaluationScarceDataset .
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