Healthy Twitter discussions? Time will tell

Studying misinformation and how to deal with unhealthy behaviours within online discussions has recently become an important field of research within social studies. With the rapid development of social media, and the increasing amount of available information and sources, rigorous manual analysis of such discourses has become unfeasible. Many approaches tackle the issue by studying the semantic and syntactic properties of discussions following a supervised approach, for example using natural language processing on a dataset labeled for abusive, fake or bot-generated content. Solutions based on the existence of a ground truth are limited to those domains which may have ground truth. However, within the context of misinformation, it may be difficult or even impossible to assign labels to instances. In this context, we consider the use of temporal dynamic patterns as an indicator of discussion health. Working in a domain for which ground truth was unavailable at the time (early COVID-19 pandemic discussions) we explore the characterization of discussions based on the the volume and time of contributions. First we explore the types of discussions in an unsupervised manner, and then characterize these types using the concept of ephemerality, which we formalize. In the end, we discuss the potential use of our ephemerality definition for labeling online discourses based on how desirable, healthy and constructive they are.This work is supported by the scheme ‘INFRAIA-01-2018-2019: Research and Innovation action’, Grant Agreement n. 871042 ‘SoBigData++: European Integrated Infrastructure for Social Mining and Big Data Analytics’Preprin

Designing smart ITS services through innovative data analysis modeling

Nowadays, one of the most important problems in urban areas concerns traffic congestion. This, in turn, has an impact on the economy, nature, human health, city architecture, and many other facets of life. Part of the vehicular traffic in cities is caused by parking space availability. The drivers of private vehicles usually want to leave their vehicles as close as possible to their destination. However, the parking slots are limited and may not be enough to sustain the demand, especially when the destination pertains to an attractive area. Thus, individuals looking for a place to park their vehicles contribute to increasing traffic flow density on roads where the parking demand cannot be satisfied. An Internet of Things (IoT) approach allows us to know the state of the parking system (availability of the parking slots) in real time through wireless networks of sensor devices. An intelligent treatment of this data could generate forecasted information that may be useful in improving management of on-street parking, thus having a notable effect on urban traffic. Smart parking systems first appeared in 2015, with IoT platforms in Santander, San Francisco and Melbourne. That is the year when those cities began to provide on-street real-time parking data in order to offer new services to their citizens. One of the most interesting services that these kinds of platforms can offer is parking availability forecasting, for which the first works in this field studied the temporal and spatial correlations of parking occupancy to support short-term forecasts (no more than 30 minutes). Those short-term forecasts are not useful at all to the end user of this service; thus, the necessary prediction intervals should be at the order of magnitude of hours. In this context, this thesis focuses on using parking and other sources of data to characterize and model different parking systems. The methodology used employs novel techniques for providing real-time forecasts of parking availability based on data from sensors with certain inaccuracies due to their mechanical nature. The models are developed from four different methodologies: ARIMA, multilayer perceptron (MLP), long-short term memory (LSTM) and gated recurrent unit (GRU). The first has been the standard approach to forecasting in the ITS literature, while the latter ones have proven to be the best neural network (NN) architectures for solving a wide set of sequential data problems, such as those presented in this work. As far as we know, LSTM and GRU methods (recurrent neural network approaches) have been used recently with good results in traffic forecasting, but not for parking. In addition, we propose using exogenous data such as weather conditions and calendar effects, thereby converting the problem from univariate to multivariate. It is shown here how NN methods naturally handle the increased complexity in the problem. The reason for using exogenous variables is that they can offer relevant information that cannot be inferred from the sensor measurements. The proposed methods have been intensively compared by creating parking models for parking sectors in five cities around the world. The results have been analysed in order to identify and provide exhaustive guidelines and insights into the inner mechanisms of parking systems while also ascertaining how the idiosyncrasies of each method are reflected in the model forecasts. When comparing the results according to their disciplines of origin (ARIMA from statistics and NN methods from machine learning), neither of the proposed methodologies is clearly better than the other, as both can provide forecasts with low error but by different means. ARIMA has shown lower error rates in small-sized sectors where the more recent status of the parking system is more relevant; while the NN methods are more capable of providing forecasts for large-sized sectors where patterns are dependent on long time horizons.En la actualidad uno de los mayores problemas de las zonas urbanas tiene origen en la congestión del tráfico con un alto impacto en la economía, el medio ambiente, la salud y otras facetas de la vida urbana. En muchas ocasiones parte de la congestión del trafico tiene origen en la disponibilidad de las plazas de aparcamiento debido a que los conductores de vehículos privados suelen querer aparcar sus vehículos lo más cerca posible de su destino pero las plazas de aparcamiento son limitadas y pueden no ser suficientes para mantener la demanda. Un enfoque basado en el Internet of Things (IoT) nos permite en tiempo real conocer la disponibilidad de plazas de estacionamiento a través de redes inalámbricas de sensores. Un tratamiento inteligente sobre estos datos puede generar información que ayude a predecir la futura demanda de estacionamiento en las zonas sensorizadas mejorando así la gestión del estacionamiento y teniendo un efecto en el tráfico urbano. Los primeros trabajos académicos en este área se centraron en estudiar las correlaciones temporales y espaciales de la ocupación del estacionamiento para proveer pronósticos a corto plazo (predicciones a tiempo máximo de 30 minutos) y que en muchas ocasiones no son de utilidad ya que para el usuario final es preferible tener estimaciones de la disponibilidad de estacionamiento en el order de magnitud de horas. En este contexto, esta tesis se centra en el uso de datos de aparcamientos y otras fuentes para caracterizar y modelizar diferentes sistemas de aparcamiento. La metodología utilizada emplea técnicas innovadoras para proporcionar predicciones en tiempo real sobre la disponibilidad de aparcamiento basadas en datos de sensores. Los modelos se desarrollan a partir de cuatro metodología: Autoregressive Integrated Moving Average (ARIMA), Multilayer Perceptron (MLP), Long-Short Term Memory (LSTM) y Gated Recurrent Unit (GRU). La primera ha sido el enfoque estándar de predicción en la literatura sobre Sistemas de Transporte Inteligentes, mientras que las otras tres han demostrado ser las mejores arquitecturas de redes neuronales para resolver un amplio conjunto de problemas de datos de naturaleza secuencial, como los que se tratan en este trabajo. Hasta donde sabemos, los métodos LSTM y GRU (enfoques de redes neuronales recurrentes) se han utilizado recientemente para la predicción de tráfico, obteniendo buenos resultados, pero no para aparcamiento. Además, proponemos utilizar datos exógenos como las condiciones meteorológicas y los efectos del calendario, transformando el problema de univariante a multivariante y demostramos como los métodos de redes neuronales gestionan de forma natural esta mayor complejidad del problema. El motivo para incluir variables exógenas es el de reducir la incertidumbre dada por las mediciones de los sensores ya que el uso de los sistemas de aparcamiento está condicionado por procesos no medibles por estos. Los métodos propuestos se han comparado mediante la creación de modelos para sectores de aparcamiento en cinco ciudades. Los resultados se han analizado con el fin de identificar y proporcionar pautas exhaustivas y conocimientos sobre los mecanismos internos de los sistemas de estacionamiento y, al mismo tiempo, determinar cómo se reflejan las idiosincrasias de cada método y de cada sector en los pronósticos del modelo. Al comparar los resultados según sus disciplinas de origen (ARIMA de estadística y redes neuronales de aprendizaje automático), ninguna de las metodologías propuestas es claramente mejor que las otras, ya que ambas pueden proporcionar predicciones con bajo error. ARIMA ha demostrado tener tasas de error más bajas en sectores de aparcamiento de menor dimensión donde el estado más reciente del sistema es más relevante; mientras que los métodos de redes neuronales has demostrado ser capaces de proporcionar mejores predicciones para sectores de gran tamaño donde los patrones tienen mayores dependencias temporalesEstadística i investigació operativ

Designing smart ITS services through innovative data analysis modeling

Nowadays, one of the most important problems in urban areas concerns traffic congestion. This, in turn, has an impact on the economy, nature, human health, city architecture, and many other facets of life. Part of the vehicular traffic in cities is caused by parking space availability. The drivers of private vehicles usually want to leave their vehicles as close as possible to their destination. However, the parking slots are limited and may not be enough to sustain the demand, especially when the destination pertains to an attractive area. Thus, individuals looking for a place to park their vehicles contribute to increasing traffic flow density on roads where the parking demand cannot be satisfied. An Internet of Things (IoT) approach allows us to know the state of the parking system (availability of the parking slots) in real time through wireless networks of sensor devices. An intelligent treatment of this data could generate forecasted information that may be useful in improving management of on-street parking, thus having a notable effect on urban traffic. Smart parking systems first appeared in 2015, with IoT platforms in Santander, San Francisco and Melbourne. That is the year when those cities began to provide on-street real-time parking data in order to offer new services to their citizens. One of the most interesting services that these kinds of platforms can offer is parking availability forecasting, for which the first works in this field studied the temporal and spatial correlations of parking occupancy to support short-term forecasts (no more than 30 minutes). Those short-term forecasts are not useful at all to the end user of this service; thus, the necessary prediction intervals should be at the order of magnitude of hours. In this context, this thesis focuses on using parking and other sources of data to characterize and model different parking systems. The methodology used employs novel techniques for providing real-time forecasts of parking availability based on data from sensors with certain inaccuracies due to their mechanical nature. The models are developed from four different methodologies: ARIMA, multilayer perceptron (MLP), long-short term memory (LSTM) and gated recurrent unit (GRU). The first has been the standard approach to forecasting in the ITS literature, while the latter ones have proven to be the best neural network (NN) architectures for solving a wide set of sequential data problems, such as those presented in this work. As far as we know, LSTM and GRU methods (recurrent neural network approaches) have been used recently with good results in traffic forecasting, but not for parking. In addition, we propose using exogenous data such as weather conditions and calendar effects, thereby converting the problem from univariate to multivariate. It is shown here how NN methods naturally handle the increased complexity in the problem. The reason for using exogenous variables is that they can offer relevant information that cannot be inferred from the sensor measurements. The proposed methods have been intensively compared by creating parking models for parking sectors in five cities around the world. The results have been analysed in order to identify and provide exhaustive guidelines and insights into the inner mechanisms of parking systems while also ascertaining how the idiosyncrasies of each method are reflected in the model forecasts. When comparing the results according to their disciplines of origin (ARIMA from statistics and NN methods from machine learning), neither of the proposed methodologies is clearly better than the other, as both can provide forecasts with low error but by different means. ARIMA has shown lower error rates in small-sized sectors where the more recent status of the parking system is more relevant; while the NN methods are more capable of providing forecasts for large-sized sectors where patterns are dependent on long time horizons.En la actualidad uno de los mayores problemas de las zonas urbanas tiene origen en la congestión del tráfico con un alto impacto en la economía, el medio ambiente, la salud y otras facetas de la vida urbana. En muchas ocasiones parte de la congestión del trafico tiene origen en la disponibilidad de las plazas de aparcamiento debido a que los conductores de vehículos privados suelen querer aparcar sus vehículos lo más cerca posible de su destino pero las plazas de aparcamiento son limitadas y pueden no ser suficientes para mantener la demanda. Un enfoque basado en el Internet of Things (IoT) nos permite en tiempo real conocer la disponibilidad de plazas de estacionamiento a través de redes inalámbricas de sensores. Un tratamiento inteligente sobre estos datos puede generar información que ayude a predecir la futura demanda de estacionamiento en las zonas sensorizadas mejorando así la gestión del estacionamiento y teniendo un efecto en el tráfico urbano. Los primeros trabajos académicos en este área se centraron en estudiar las correlaciones temporales y espaciales de la ocupación del estacionamiento para proveer pronósticos a corto plazo (predicciones a tiempo máximo de 30 minutos) y que en muchas ocasiones no son de utilidad ya que para el usuario final es preferible tener estimaciones de la disponibilidad de estacionamiento en el order de magnitud de horas. En este contexto, esta tesis se centra en el uso de datos de aparcamientos y otras fuentes para caracterizar y modelizar diferentes sistemas de aparcamiento. La metodología utilizada emplea técnicas innovadoras para proporcionar predicciones en tiempo real sobre la disponibilidad de aparcamiento basadas en datos de sensores. Los modelos se desarrollan a partir de cuatro metodología: Autoregressive Integrated Moving Average (ARIMA), Multilayer Perceptron (MLP), Long-Short Term Memory (LSTM) y Gated Recurrent Unit (GRU). La primera ha sido el enfoque estándar de predicción en la literatura sobre Sistemas de Transporte Inteligentes, mientras que las otras tres han demostrado ser las mejores arquitecturas de redes neuronales para resolver un amplio conjunto de problemas de datos de naturaleza secuencial, como los que se tratan en este trabajo. Hasta donde sabemos, los métodos LSTM y GRU (enfoques de redes neuronales recurrentes) se han utilizado recientemente para la predicción de tráfico, obteniendo buenos resultados, pero no para aparcamiento. Además, proponemos utilizar datos exógenos como las condiciones meteorológicas y los efectos del calendario, transformando el problema de univariante a multivariante y demostramos como los métodos de redes neuronales gestionan de forma natural esta mayor complejidad del problema. El motivo para incluir variables exógenas es el de reducir la incertidumbre dada por las mediciones de los sensores ya que el uso de los sistemas de aparcamiento está condicionado por procesos no medibles por estos. Los métodos propuestos se han comparado mediante la creación de modelos para sectores de aparcamiento en cinco ciudades. Los resultados se han analizado con el fin de identificar y proporcionar pautas exhaustivas y conocimientos sobre los mecanismos internos de los sistemas de estacionamiento y, al mismo tiempo, determinar cómo se reflejan las idiosincrasias de cada método y de cada sector en los pronósticos del modelo. Al comparar los resultados según sus disciplinas de origen (ARIMA de estadística y redes neuronales de aprendizaje automático), ninguna de las metodologías propuestas es claramente mejor que las otras, ya que ambas pueden proporcionar predicciones con bajo error. ARIMA ha demostrado tener tasas de error más bajas en sectores de aparcamiento de menor dimensión donde el estado más reciente del sistema es más relevante; mientras que los métodos de redes neuronales has demostrado ser capaces de proporcionar mejores predicciones para sectores de gran tamaño donde los patrones tienen mayores dependencias temporalesPostprint (published version

Characterizing parking systems from sensor data through a data-driven approach

Nowadays, urban traffic affects the quality of life in cities as the problem becomes even more exacerbated by parking issues: congestion increases due to drivers searching slots to park. An Internet of Things approach permits drivers to know the parking availability in real time and provides data that can be used to develop predictive models. This can be useful in improving the management of parking areas while having an important effect on traffic. This work begins by describing the state-of-the-art parking predictive models and, then, introduces the recurrent neural network methods that were used Long Short-Term Memory and Gated Recurrent Unit, as well as the models developed according to real scenarios in Wattens and Los Angeles. To improve the quality of the models, exogenous variables related to weather and calendar are considered. Finally, the results are described, followed by suggestions for future research.This research was funded by Secretaria d’Universitats i Recerca de la Generalitat de Catalunya [2017-SGR-1749] and under the Industrial Doctorate Program [2016-DI-79].Peer ReviewedPostprint (author's final draft

Improving parking availability information using deep learning techniques

Urban traffic currently affects the quality of life in cities and metropolitan areas as the problem becomes ever more aggravated by parking issues: congestion increases due to individuals looking for slotsto park their vehicles. An Internet of Things approach allows drivers to know the state of the parkingsystem in real time through wireless networks of sensor devices. This work focuses on studying the data generated by parking systems in order to develop predictive models that generate forecasted information. This can be useful in improving the managementof parking areas, especially on-streetparking, while having an important effect on urban traffic. This research begins by looking at thestate of the art in predictive methods based on machine learning for time series. Similar studies and proposed solutions for parking predictionare described in terms of the technology and current state-of-the-art predictive models. This paper then introduces the recurrent neural network methodsthat were usedin this research,namely Long Short-Term Memory and Gated Recurrent Unit, as well as the models developed according to real scenarios in different cities. In order to improve the quality of the models, exogenous variables like hourly weather and calendar effects are taken into account,and the baseline models are compared to the models that usedthis information. Finally, the preliminary encouraging results are described, followed by suggestions for corresponding future workPostprint (published version

A comparison of deep learning methods for urban traffic forecasting using floating car data

Cities today must address the challenge of sustainable mobility, and traffic state forecasting plays a key role in mitigating traffic congestion in urban areas. For example, predicting path travel time is a crucial issue in navigation and route planning applications. Furthermore, the pervasive penetration of information and communication technologies makes floating car data an important source of real-time data for intelligent transportation system applications. This paper deals with the problem of forecasting urban traffic when floating car data is available. A comparison of four deep learning methods is presented to demonstrate the capabilities of the neural network approaches (recurrent and/or convolutional) in solving the traffic forecasting problem in an urban context. Different tests are proposed in order to not only evaluate the developed deep learning models, but also to analyze how the penetration rates of floating cars affect forecasting accuracy. The presented experiments were designed according to a microscopic traffic simulation approach in order to emulate floating car data fleets, which provide vehicle position and speed, and to validate the obtained results. Finally, some conclusions and further research are presented.Postprint (published version

Multiomics reveals glutathione metabolism as a driver of bimodality during stem cell aging.

With age, skeletal muscle stem cells (MuSCs) activate out of quiescence more slowly and with increased death, leading to defective muscle repair. To explore the molecular underpinnings of these defects, we combined multiomics, single-cell measurements, and functional testing of MuSCs from young and old mice. The multiomics approach allowed us to assess which changes are causal, which are compensatory, and which are simply correlative. We identified glutathione (GSH) metabolism as perturbed in old MuSCs, with both causal and compensatory components. Contrary to young MuSCs, old MuSCs exhibit a population dichotomy composed of GSHhigh cells (comparable with young MuSCs) and GSHlow cells with impaired functionality. Mechanistically, we show that antagonism between NRF2 and NF-κB maintains this bimodality. Experimental manipulation of GSH levels altered the functional dichotomy of aged MuSCs. These findings identify a novel mechanism of stem cell aging and highlight glutathione metabolism as an accessible target for reversing MuSC aging