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La Unión Europea ha propuesto un nuevo paquete de sanciones contra Rusia que incluye un embargo al petróleo ruso. El embargo se aplicaría gradualmente durante seis meses para el petróleo crudo y ocho meses para los productos refinados. El objetivo es aumentar la presión sobre Rusia para que ponga fin a su invasión de Ucrania.
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Cada vez estamos viendo más y más aparatos "inteligentes" se cuelan en nuestros hogares. Ya sean bombillas, enchufes o sensores de temperatura, lo que tienen casi todos en común es la necesidad de estar conectados a un hub central específico de cada marca así como una aplicación móvil para configurarlos. Además, una de las características más anunciada es su integración con Alexa o Google Assistant. Todo esto implica por lo general que nuestros aparatos van a estar conectados permanentemente a internet y enviando nuestros datos a una o mútiples compañías.
¿Existen alternativas para disfrutar de esta tecnología en casa sin "hipotecarnos" con los dispositivos de una compañía concreta y para tener el control de nuestros datos? En esta charla contaré mi experiencia domotizando mi casa manteniendo la privacidad.
Automatizando el aprendizaje basado en datos
En los últimos años ha habido un creciente interés en extraer información útil de grandes cantidades
de datos. Esta información se puede usar para hacer predicciones a futuro o inferir valores
desconocidos. Existen una gran variedad de modelos predictivos para problemas de clasificación y
regresión. Sin embargo, en muchas investigaciones se asume a menudo que los datos están limpios
y se presta poca atención al preprocesamiento de los datos. A pesar de que hay muchos métodos
para solventar tareas de preprocesamiento específicas (por ejemplo, detección de valores extremos o
selección de atributos), el esfuerzo para realizar el preprocesamiento y limpiado de los datos puede
llevar entre el 60% y el 80% de todo el tiempo empleado en el proceso de minería de datos. Se hace por tanto muy necesario automatizar todo o parte de este proceso. En esta charla se da un introducción a la automatización de la selección y optimización de múltiples
métodos de preprocesamiento y predicción.
Modelling Multi-Component Predictive Systems as Petri Nets
Building reliable data-driven predictive systems requires a considerable amount of human effort, especially in the data preparation and cleaning phase. In many application domains, multiple preprocessing steps need to be applied in sequence, constituting a `workflow' and facilitating reproducibility. The concatenation of such workflow with a predictive model forms a Multi-Component Predictive System (MCPS). Automatic MCPS composition can speed up this process by taking the human out of the loop, at the cost of model transparency (i.e. not being comprehensible by human experts). In this paper, we adopt and suitably re-define the Well-handled with Regular Iterations Work Flow (WRI-WF) Petri nets to represent MCPSs. The use of such WRI-WF nets helps to increase the transparency of MCPSs required in industrial applications and make it possible to automatically verify the composed workflows. We also present our experience and results of applying this representation to model soft sensors in chemical production plants.
Brand engagement with mobile gamification apps from a developer perspective
Excelling at what your company offer is often synonymous of success, but having a loyal customer base is not easy. Applying gamification elements to products or services can help brands to keep customers engaged, but it's not exempt of risks. This talk will present an introduction to gamification and will show success stories, specially focusing on apps promoting a positive behaviour change. Manuel will also share some lessons learned from app development and what opportunities gamification can bring to multiple disciplines.
Effects of change propagation resulting from adaptive preprocessing in multic...
Predictive modelling is a complex process that requires a number of steps to transform raw data into predictions. Preprocessing of the input data is a key step in such process, and the selection of proper preprocessing methods is often a labour intensive task. Such methods are usually trained offline and their parameters remain fixed during the whole model deployment lifetime. However, preprocessing of non-stationary data streams is more challenging since the lack of adaptation of such preprocessing methods may degrade system performance. In addition, dependencies between different predictive system components make the adaptation process more challenging. In this paper we discuss the effects of change propagation resulting from using adaptive preprocessing in a Multicomponent Predictive System (MCPS). To highlight various issues we present four scenarios with different levels of adaptation. A number of experiments have been performed with a range of datasets to compare the prediction error in all four scenarios. Results show that well managed adaptation considerably improves the prediction performance. However, the model can become inconsistent if adaptation in one component is not correctly propagated throughout the rest of system components. Sometimes, such inconsistency may not cause an obvious deterioration in the system performance, therefore being difficult to detect. In some other cases it may even lead to a system failure as was observed in our experiments.
Improving transport timetables usability for mobile devices
The increasing number of passengers using mobile devices like smartphones or tablets in last few years have motivated transport companies to develop mobile websites and apps for their customers. However, the transition from desktop to mobile versions is challenging and many websites are still not optimised for user experience on such devices. In this paper we present a usability study carried out with the timetables of Nottingham City Transport website. A number of design changes have improved the overall user experience as confirmed by the results.
Automating Machine Learning - Is it feasible?
Facing a machine learning problem for the first time can be overwhelming. Hundreds of methods exist for tackling problems such as classification, regression or clustering. Selecting the appropriate method is challenging, specially if no much prior knowledge is known. In addition, most models require to optimise a number of hyperparameters to perform well. Preparing the data for the learning algorithm is also a labour-intensive process that includes cleaning outliers and imperfections, feature selection, data transformation like PCA and more. A workflow connecting preprocessing methods and predictive models is called a multicomponent predictive system (MCPS). This talk introduces the problem of automating the composition and optimisation of MCPSs and also how they can be adapted in changing environments.
Recomendados
Hogar (Des)Conectado
Cada vez estamos viendo más y más aparatos "inteligentes" se cuelan en nuestros hogares. Ya sean bombillas, enchufes o sensores de temperatura, lo que tienen casi todos en común es la necesidad de estar conectados a un hub central específico de cada marca así como una aplicación móvil para configurarlos. Además, una de las características más anunciada es su integración con Alexa o Google Assistant. Todo esto implica por lo general que nuestros aparatos van a estar conectados permanentemente a internet y enviando nuestros datos a una o mútiples compañías.
¿Existen alternativas para disfrutar de esta tecnología en casa sin "hipotecarnos" con los dispositivos de una compañía concreta y para tener el control de nuestros datos? En esta charla contaré mi experiencia domotizando mi casa manteniendo la privacidad.
Automatizando el aprendizaje basado en datos
En los últimos años ha habido un creciente interés en extraer información útil de grandes cantidades
de datos. Esta información se puede usar para hacer predicciones a futuro o inferir valores
desconocidos. Existen una gran variedad de modelos predictivos para problemas de clasificación y
regresión. Sin embargo, en muchas investigaciones se asume a menudo que los datos están limpios
y se presta poca atención al preprocesamiento de los datos. A pesar de que hay muchos métodos
para solventar tareas de preprocesamiento específicas (por ejemplo, detección de valores extremos o
selección de atributos), el esfuerzo para realizar el preprocesamiento y limpiado de los datos puede
llevar entre el 60% y el 80% de todo el tiempo empleado en el proceso de minería de datos. Se hace por tanto muy necesario automatizar todo o parte de este proceso. En esta charla se da un introducción a la automatización de la selección y optimización de múltiples
métodos de preprocesamiento y predicción.
Modelling Multi-Component Predictive Systems as Petri Nets
Building reliable data-driven predictive systems requires a considerable amount of human effort, especially in the data preparation and cleaning phase. In many application domains, multiple preprocessing steps need to be applied in sequence, constituting a `workflow' and facilitating reproducibility. The concatenation of such workflow with a predictive model forms a Multi-Component Predictive System (MCPS). Automatic MCPS composition can speed up this process by taking the human out of the loop, at the cost of model transparency (i.e. not being comprehensible by human experts). In this paper, we adopt and suitably re-define the Well-handled with Regular Iterations Work Flow (WRI-WF) Petri nets to represent MCPSs. The use of such WRI-WF nets helps to increase the transparency of MCPSs required in industrial applications and make it possible to automatically verify the composed workflows. We also present our experience and results of applying this representation to model soft sensors in chemical production plants.
Brand engagement with mobile gamification apps from a developer perspective
Excelling at what your company offer is often synonymous of success, but having a loyal customer base is not easy. Applying gamification elements to products or services can help brands to keep customers engaged, but it's not exempt of risks. This talk will present an introduction to gamification and will show success stories, specially focusing on apps promoting a positive behaviour change. Manuel will also share some lessons learned from app development and what opportunities gamification can bring to multiple disciplines.
Effects of change propagation resulting from adaptive preprocessing in multic...
Predictive modelling is a complex process that requires a number of steps to transform raw data into predictions. Preprocessing of the input data is a key step in such process, and the selection of proper preprocessing methods is often a labour intensive task. Such methods are usually trained offline and their parameters remain fixed during the whole model deployment lifetime. However, preprocessing of non-stationary data streams is more challenging since the lack of adaptation of such preprocessing methods may degrade system performance. In addition, dependencies between different predictive system components make the adaptation process more challenging. In this paper we discuss the effects of change propagation resulting from using adaptive preprocessing in a Multicomponent Predictive System (MCPS). To highlight various issues we present four scenarios with different levels of adaptation. A number of experiments have been performed with a range of datasets to compare the prediction error in all four scenarios. Results show that well managed adaptation considerably improves the prediction performance. However, the model can become inconsistent if adaptation in one component is not correctly propagated throughout the rest of system components. Sometimes, such inconsistency may not cause an obvious deterioration in the system performance, therefore being difficult to detect. In some other cases it may even lead to a system failure as was observed in our experiments.
Improving transport timetables usability for mobile devices
The increasing number of passengers using mobile devices like smartphones or tablets in last few years have motivated transport companies to develop mobile websites and apps for their customers. However, the transition from desktop to mobile versions is challenging and many websites are still not optimised for user experience on such devices. In this paper we present a usability study carried out with the timetables of Nottingham City Transport website. A number of design changes have improved the overall user experience as confirmed by the results.
Automating Machine Learning - Is it feasible?
Facing a machine learning problem for the first time can be overwhelming. Hundreds of methods exist for tackling problems such as classification, regression or clustering. Selecting the appropriate method is challenging, specially if no much prior knowledge is known. In addition, most models require to optimise a number of hyperparameters to perform well. Preparing the data for the learning algorithm is also a labour-intensive process that includes cleaning outliers and imperfections, feature selection, data transformation like PCA and more. A workflow connecting preprocessing methods and predictive models is called a multicomponent predictive system (MCPS). This talk introduces the problem of automating the composition and optimisation of MCPSs and also how they can be adapted in changing environments.
Towards Automatic Composition of Multicomponent Predictive Systems
Automatic composition and parametrisation of multicomponent predictive systems (MCPSs) consisting of chains of data transformation steps is a challenging task. In this paper we propose and describe an extension to the Auto-WEKA software which now allows to compose and optimise such flexible MCPSs by using a sequence of WEKA methods. In the experimental analysis we focus on examining the impact of significantly extending the search space by incorporating additional hyperparameters of the models, on the quality of the found solutions. In a range of extensive experiments three different optimisation strategies are used to automatically compose MCPSs on 21 publicly available datasets. A comparison with previous work indicates that extending the search space improves the classification accuracy in the majority of the cases. The diversity of the found MCPSs are also an indication that fully and automatically exploiting different combinations of data cleaning and preprocessing techniques is possible and highly beneficial for different predictive models. This can have a big impact on high quality predictive models development, maintenance and scalability aspects needed in modern application and deployment scenarios.
From sensor readings to prediction: on the process of developing practical so...
Automatic data acquisition systems provide large amounts of streaming data generated by physical sensors. This data forms an input to computational models (soft sensors) routinely used for monitoring and control of industrial processes, traffic patterns, environment and natural hazards, and many more. The majority of these models assume that the data comes in a cleaned and pre-processed form, ready to be fed directly into a predictive model. In practice, to ensure appropriate data quality, most of the modelling efforts concentrate on preparing data from raw sensor readings to be used as model inputs. This study analyzes the process of data preparation for predictive models with streaming sensor data. We present the challenges of data preparation as a four-step process, identify the key challenges in each step, and provide recommendations for handling these issues. The discussion is focused on the approaches that are less commonly used, while, based on our experience, may contribute particularly well to solving practical soft sensor tasks. Our arguments are illustrated with a case study in the chemical production industry.
Quick presentation for the OpenML workshop in Eindhoven 2014
Personal introduction and workplan for the OpenML workshop that took place in TU/e (Eindhoven)
Online Detection of Shutdown Periods in Chemical Plants: A Case Study
In process industry, chemical processes are controlled and monitored by using readings from multiple physical sensors across the plants. Such physical sensors are also supplemented by soft sensors, i.e. adaptive predictive models, which are often used for computing hard-to-measure variables of the process. For soft sensors to work well and adapt to changing operating conditions they need to be provided with relevant data. As production plants are regularly stopped, data instances generated during shutdown periods have to be identified to avoid updating these predictive models with wrong data. We present a case study concerned with a large chemical plant operation over a 2 years period. The task is to robustly and accurately identify the shutdown periods even in case of multiple sensor failures. State-of-the-art methods were evaluated using the first half of the dataset for calibration purposes and the other half for measuring the performance. Results show that shutdowns (i.e. sudden changes) can be quickly detected in any case but the detection delay of startups (i.e. gradual changes) is directly related with the choice of a window size.
Artificial Intelligence for Automating Data Analysis
The requirements for analysing big volumes of data have increased over the last few decades. The process of selecting, cleaning, modelling and interpreting data is called the KDD process. The decision of how to approach each step in this process has often been made manually by experts. However, experts cannot be aware of all methods, nor is it feasible to try all of them. Researchers have proposed different approaches for automating, or at least advising, the stages of the KDD process. This talk will outline the different types of Intelligent Discovery Assistants as described in the work of Serban et al. “A survey of intelligent assistants for data analysis” and point out some future directions.
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Towards Automatic Composition of Multicomponent Predictive Systems
Automatic composition and parametrisation of multicomponent predictive systems (MCPSs) consisting of chains of data transformation steps is a challenging task. In this paper we propose and describe an extension to the Auto-WEKA software which now allows to compose and optimise such flexible MCPSs by using a sequence of WEKA methods. In the experimental analysis we focus on examining the impact of significantly extending the search space by incorporating additional hyperparameters of the models, on the quality of the found solutions. In a range of extensive experiments three different optimisation strategies are used to automatically compose MCPSs on 21 publicly available datasets. A comparison with previous work indicates that extending the search space improves the classification accuracy in the majority of the cases. The diversity of the found MCPSs are also an indication that fully and automatically exploiting different combinations of data cleaning and preprocessing techniques is possible and highly beneficial for different predictive models. This can have a big impact on high quality predictive models development, maintenance and scalability aspects needed in modern application and deployment scenarios.
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Automatic data acquisition systems provide large amounts of streaming data generated by physical sensors. This data forms an input to computational models (soft sensors) routinely used for monitoring and control of industrial processes, traffic patterns, environment and natural hazards, and many more. The majority of these models assume that the data comes in a cleaned and pre-processed form, ready to be fed directly into a predictive model. In practice, to ensure appropriate data quality, most of the modelling efforts concentrate on preparing data from raw sensor readings to be used as model inputs. This study analyzes the process of data preparation for predictive models with streaming sensor data. We present the challenges of data preparation as a four-step process, identify the key challenges in each step, and provide recommendations for handling these issues. The discussion is focused on the approaches that are less commonly used, while, based on our experience, may contribute particularly well to solving practical soft sensor tasks. Our arguments are illustrated with a case study in the chemical production industry.
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In process industry, chemical processes are controlled and monitored by using readings from multiple physical sensors across the plants. Such physical sensors are also supplemented by soft sensors, i.e. adaptive predictive models, which are often used for computing hard-to-measure variables of the process. For soft sensors to work well and adapt to changing operating conditions they need to be provided with relevant data. As production plants are regularly stopped, data instances generated during shutdown periods have to be identified to avoid updating these predictive models with wrong data. We present a case study concerned with a large chemical plant operation over a 2 years period. The task is to robustly and accurately identify the shutdown periods even in case of multiple sensor failures. State-of-the-art methods were evaluated using the first half of the dataset for calibration purposes and the other half for measuring the performance. Results show that shutdowns (i.e. sudden changes) can be quickly detected in any case but the detection delay of startups (i.e. gradual changes) is directly related with the choice of a window size.
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The requirements for analysing big volumes of data have increased over the last few decades. The process of selecting, cleaning, modelling and interpreting data is called the KDD process. The decision of how to approach each step in this process has often been made manually by experts. However, experts cannot be aware of all methods, nor is it feasible to try all of them. Researchers have proposed different approaches for automating, or at least advising, the stages of the KDD process. This talk will outline the different types of Intelligent Discovery Assistants as described in the work of Serban et al. “A survey of intelligent assistants for data analysis” and point out some future directions.
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lámparas más eficientes del mercado, debido a su menor consumo y por la cantidad de luz que emiten. Adquieren una vida útil de hasta 50.000 horas y no generan calor alguna. Si quieres cambiar la iluminación de tu hogar para hacerla mucho más eficiente, ¡esta es tu mejor opción!
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Lámpara incandescente.
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Lámpara LED.
Lámpara de plasma.
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