112 research outputs found
A methodology for the resolution of cashtag collisions on Twitter – A natural language processing & data fusion approach
Investors utilise social media such as Twitter as a means of sharing news surrounding financials stocks
listed on international stock exchanges. Company ticker symbols are used to uniquely identify companies
listed on stock exchanges and can be embedded within tweets to create clickable hyperlinks referred to
as cashtags, allowing investors to associate their tweets with specific companies. The main limitation is
that identical ticker symbols are present on exchanges all over the world, and when searching for such
cashtags on Twitter, a stream of tweets is returned which match any company in which the cashtag
refers to - we refer to this as a cashtag collision. The presence of colliding cashtags could sow confusion
for investors seeking news regarding a specific company. A resolution to this issue would benefit investors
who rely on the speediness of tweets for financial information, saving them precious time. We propose
a methodology to resolve this problem which combines Natural Language Processing and Data Fusion
to construct company-specific corpora to aid in the detection and resolution of colliding cashtags, so
that tweets can be classified as being related to a specific stock exchange or not. Supervised machine
learning classifiers are trained twice on each tweet – once on a count vectorisation of the tweet text,
and again with the assistance of features contained in the company-specific corpora. We validate the
cashtag collision methodology by carrying out an experiment involving companies listed on the London
Stock Exchange. Results show that several machine learning classifiers benefit from the use of the custom
corpora, yielding higher classification accuracy in the prediction and resolution of colliding cashtags
Análisis Urbano y Comunidades Inteligentes: Una Aproximación al Empleo de la Tecnología en la Movilidad Cotidiana
Concentration of population in urban centers is a global problem for which different strategies in order to organize different processes in cities and improve the quality of life are required. The creation of smart communities is shown as a sustainable solution since they deal with various key aspects, such as traffic management and mobility, through the use of information technologies (ITs). This work presents a review of recent studies using information technologies for urban analysis and mobility in cities. A descriptive analysis of automated methods for collecting and analyzing citizens’ mobility patterns is performed; it is centered in smart card use, geolocation and geotagging. It is concluded that a robust communication infrastructure, supported by an efficient computational platform allowing big data management and ubiquitous computing, is a crucial aspect for urban management in a smart communityLa concentración de la población en los centros urbanos es una problemática mundial que requiere de estrategias que permitan organizar sus procesos y mejorar la calidad de vida. La creación de comunidades inteligentes se muestra como una solución sostenible, debido a que éstas trabajan aspectos claves para el desarrollo urbano, como la gestión de tráfico y la movilidad, apoyada en las tecnologías de la información (TICs). Este trabajo presenta una revisión del estado del arte en cuanto a la aplicación de las TICs al análisis urbano y movilidad ciudadana. Se analizan descriptivamente diversos métodos automáticos para la recolección y el análisis del patrón de movilidad de los ciudadanos, enfocándose en el uso de tarjetas inteligentes, geolocalización y geoetiquetado. Se encuentra que una infraestructura de comunicaciones robusta, apoyada en una plataforma computacional ágil con manejo de grandes datos y computación ubicua, es primordial para la gestión urbana en una comunidad inteligente
Análisis urbano y comunidades inteligentes: “una aproximación al empleo de la tecnología en la movilidad cotidiana”
Concentration of population in urban centers
is a global problem for which different strategies in
order to organize different processes in cities and improve
the quality of life are required. The creation of
smart communities is shown as a sustainable solution
since they deal with various key aspects, such as traffc
management and mobility, through the use of information
technologies (ITs). This work presents a review of
recent studies using information technologies for urban
analysis and mobility in cities. A descriptive analysis
of automated methods for collecting and analyzing citizens’
mobility patterns is performed; it is centered in
smart card use, geolocation and geotagging. It is concluded
that a robust communication infrastructure, supported
by an effcient computational platform allowing
big data management and ubiquitous computing, is a
crucial aspect for urban management in a smart community.La concentración de la población en los centros
urbanos es una problemática mundial que requiere de estrategias
que permitan organizar sus procesos y mejorar
la calidad de vida. La creación de comunidades inteligentes
se muestra como una solución sostenible, debido a que
éstas trabajan aspectos claves para el desarrollo urbano,
como la gestión de tráfco y la movilidad, apoyada en las
tecnologías de la información (TICs). Este trabajo presenta
una revisión del estado del arte en cuanto a la aplicación
de las TICs al análisis urbano y movilidad ciudadana. Se
analizan descriptivamente diversos métodos automáticos
para la recolección y el análisis del patrón de movilidad de
los ciudadanos, enfocándose en el uso de tarjetas inteligentes,
geolocalización y geoetiquetado. Se encuentra que una
infraestructura de comunicaciones robusta, apoyada en
una plataforma computacional ágil con manejo de grandes
datos y computación ubicua, es primordial para la gestión
urbana en una comunidad inteligente
Decentralized and collaborative machine learning framework for IoT
Decentralized machine learning has recently been proposed as a potential solution to the security issues of the canonical federated learning approach. In this paper, we propose a decentralized and collaborative machine learning framework specially oriented to resource-constrained devices, usual in IoT deployments. With this aim we propose the following construction blocks. First, an incremental learning algorithm based on prototypes that was specifically implemented to work in low-performance computing elements. Second, two random-based protocols to exchange the local models among the computing elements in the network. Finally, two algorithmics approaches for prediction and prototype creation. This proposal was compared to a typical centralized incremental learning approach in terms of accuracy, training time and robustness with very promising results.Axencia Galega de Innovación | Ref. 25/IN606D/2021/2612348Agencia Estatal de Investigación | Ref. PID2020-113795RB-C3
Analysis of the NS-3 module QKDNetSim for the Simulation of QKD Networks
Cursos e Congresos, C-155[Abstract] Quantum Key Distribution (QKD) is a promising technology that allows two nodes to
privately agree on a key through a quantum channel. Unfortunately, QKD is still in experimental
phase and researchers must rely on simulators to replicate the behaviour of a quantum network.
One of the most widespread is QKDNetSim, a module for the C++ network simulator NS-3.
However, this module is very limited in its behaviour, so it does not faithfully represent a real
quantum network. In this work we analyse the structure and components of QKDNetSim, as well
as its shortcomings and how they affect the quality of the simulationThis work is part of the project TED2021-130369B-C31, TED2021-130369BC32, TED2021-130369B-C33 and TED2021-130492B-C21 funded by MCIN/AEI/10.13039/501100011033 and by
“ERDF A way of making Europe”. The work is also funded by the Plan Complementario de Comunicaciones Cuánticas, Spanish
Ministry of Science and Innovation (MICINN), Plan de Recuperación NextGenerationEU de la Unión Europea (PRTR-C17.I1, CITIC Ref. 305.2022), and Regional Government of Galicia (Agencia Gallega de Innovación, GAIN, CITIC Ref. 306.2022
Towards predictive models in food engineering: Parameter estimation dos and don'ts
1 póster.-- 29th EFFoST International Conference, 10-12 November 2015, Athens, GreeceRigorous, physics based, modeling is at the core of computer aided food process engineering. Models often
require the values of some, typically unknown, parameters (thermo-physical properties, kinetic constants,
etc). Therefore, parameter estimation from experimental data is critical to achieve desired model predictive
properties. Unfortunately, it must be admitted that often experiment design and modeling are fully
separated tasks: experiments are not designed for the purpose of modeling and models are usually derived
without paying especial attention to available experimental data or experimentation capabilities. When, at
some point, the parameter estimation problem is put on the table, modelers use available experimental
data to ``manually'' tune the unknown parameters. This results in inaccurate parameter estimates, usually
experiment dependent, with the implications this has in model validation.
This work takes a new look into the parameter estimation problem in food process modeling. First the
common pitfalls in parameter estimation are described. Second we present the theoretical background and
the numerical techniques to define a parameter estimation protocol to iteratively improve model predictive
capabilities. This protocol includes: reduced order modeling, structural and practical identifiability analyses,
data fitting with global optimization methods and optimal experimental design.
And, to finish, we illustrate the performance of the proposed protocol with an example related to the
thermal processing of packaged foods. The model was experimentally validated in the IIM-CSIC pilot plantThe authors acknowledge financial support from the EU (Project SPECTRAFISH), Spanish
Ministry of Science and Innovation (Project ISFORQUALITY) and CSIC (Project CONTROLA)Peer reviewe
Color determination as a tool to detect oil contamination on sandy beaches
El color ha sido utilizado en numerosos estudios para caracterizar las
muestras de sedimento y discriminar su origen. Sin embargo, no existen estudios
sobre la influencia de la contaminación en esta propiedad física. El objetivo
de este trabajo es evaluar los cambios de color en el sedimento que
presenta las diferentes morfologías del fuel (galletas, arenas grises). El color
de las muestras seleccionadas sometidas a distintos tratamientos fue medido
usando un espectrofotómetro Konica Minolta CM-2600d. Los sedimentos
arenosos estudiados pertenecieron a dos playas (Nemiña y O Rostro), unas
de las más afectadas por el accidente del petrolero Prestige (Noviembre
2002). Este estudio puso de manifiesto la importancia del color para la adecuada
discriminación en el seguimiento de la contaminación con fuel. Nuestros
resultados demostraron la capacidad del espectrofotómetro para evidenciar
la existencia de contaminación por fuel en arenas que parecían limpias
a simple vista. Además, esta técnica fue útil para establecer el grado de contaminación
por fuel, relacionando la oscuridad del color gris con el estado
de degradación del fuel en el sedimentoColor has been used in many studies to characterize sediment samples
and to discriminate their origin. However, there are not studies about the influence
of oil contamination in this physical property. The aim of this work
is to assess the changes in color in sediments showing different types of oil
appearances (tar balls, grey sands). The color of selected samples subjected
to different treatments was measured using a Konica Minolta CM-2600d
spectrophotometer. The studied sand sediments belong to the two beaches
(Nemiña and O Rostro) most strongly affected by the Prestige oil spill (November
2002). This study highlights the interest of adequate color discrimination
for oil contamination monitoring. Our results demonstrated the ability
of spectrophotometer to evidence the occurrence of oil contamination in
sands that look clean to the naked eye. Furthermore, this technique was
also useful to establish the degree of oil contamination, linking the darkness
of the grey color to the degradation stage of the oil in the sedimen
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