End-to-End Methodological Approach for the Data-Driven Design of Customer-Centered Digital Services

2018 ◽  
pp. 208-218 ◽  
Author(s):  
Jürg Meierhofer ◽  
Anne Herrmann
Keyword(s):  
Methodological Approach ◽  
Data Driven ◽  
Digital Services ◽  
End To End

scholarly journals A Data Driven End-to-End Approach for In-the-Wild Monitoring of Eating Behavior Using Smartwatches

2021 ◽  
Vol 25 (1) ◽  
pp. 22-34 ◽  
Author(s):  
Konstantinos Kyritsis ◽  
Christos Diou ◽  
Anastasios Delopoulos
Keyword(s):  
Eating Behavior ◽  
Data Driven ◽  
In The Wild ◽  
End To End

scholarly journals Integrating Satellite Soil Moisture and Rainfall Data on a Data-Driven Model for the Assessment of Shallow Landslides Hazard

Proceedings ◽  
2019 ◽  
Vol 30 (1) ◽  
pp. 42
Author(s):  
Meisina ◽  
Bordoni ◽  
Lucchelli ◽  
Brocca ◽  
Ciabatta ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Methodological Approach ◽  
Regional Scale ◽  
Shallow Landslides ◽  
Predisposing Factors ◽  
Data Driven ◽  
Triggering Factors ◽  
Physically Based ◽  
Physically Based Models ◽  
Triggering Events

Shallow landslides are very dangerous phenomena, widespread all over the world, which could provoke significant damages to buildings, roads, facilities, cultivations and, sometimes, loss of human lives. It is then necessary assessing the most prone zones in a territory which is particularly susceptible to these phenomena and the frequency of the events, according to the return time of the triggering events, which generally correspond to intense and concentrated rainfalls. Susceptibility and hazard of a territory are usually assessed by means of physically-based models, that quantify the hydrological and the mechanical responses of the slopes according to particular rainfall amounts. Whereas, these methodologies could be applied in a reliable way in little catchments, where geotechnical and hydrological features of the materials affected by shallow failures are homogeneous. Moreover, physically-based models require, sometimes, significant computation power, which limit their implementations at regional scale. Data-driven models could overcome both of these limitations, even if they are generally built up taking into only the predisposing factors of shallow instabilities. Thus, they allow usually to estimate the susceptibility of a territory, without considering the frequency of the triggering events. It is then required to consider also triggering factors of shallow landslides to allow these methods to estimate also the hazard. This work presents the preliminary results of the development and the implementation of data-driven model able to estimate the hazard of a territory towards shallow landslides. The model is based on a Genetic Algorithm Model (GAM), which links geomorphological, hydrological, geological and land use predisposing factors to triggering factors of shallow failures. These triggering factors correspond to the soil moisture content and to the rainfall amounts, which are available for entire a study area thanks to satellite measures. The methodological approach is testing in different catchments of 30–40 km2 located in Oltrepò Pavese area (northern Italy), where detailed inventories of shallow landslides occurred during past triggering events and corresponding satellite soil moisture and rainfall maps are available. This work was made in the frame of the ANDROMEDA project, funded by Fondazione Cariplo.

scholarly journals Learning Robust Control Policies for End-to-End Autonomous Driving From Data-Driven Simulation

2020 ◽  
Vol 5 (2) ◽  
pp. 1143-1150 ◽  
Author(s):  
Alexander Amini ◽  
Igor Gilitschenski ◽  
Jacob Phillips ◽  
Julia Moseyko ◽  
Rohan Banerjee ◽  
...  
Keyword(s):  
Robust Control ◽  
Autonomous Driving ◽  
Data Driven ◽  
Control Policies ◽  
End To End

The Data Imaginaries of Climate Art: The Manifest Data Project

Leonardo ◽  
2021 ◽  
pp. 1-10
Author(s):  
Tom Corby ◽  
Gavin Baily ◽  
Jonathan Mackenzie ◽  
Giles Lane ◽  
Erin Dickson ◽  
...  
Keyword(s):  
Climate Change ◽  
Southern Ocean ◽  
Climate Models ◽  
Methodological Approach ◽  
Ways Of Knowing ◽  
Data Driven ◽  
Ocean Studies

Abstract We discuss a series of artworks produced since 2009 including The Southern Ocean Studies (2012), The Northern Polar Studies (2014) and Carbon Topographies (2020). Through this work we explore how climate models can be employed to develop data driven imaginaries of climate change, its impacts and causes. We argue for the experiential potential of this information for producing differently situated ways of knowing climate, framing this through a methodological approach described as ‘data manifestation’.

scholarly journals A Data-Driven Knowledge Acquisition System: An End-to-End Knowledge Engineering Process for Generating Production Rules

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 15587-15607 ◽  
Author(s):  
Maqbool Ali ◽  
Rahman Ali ◽  
Wajahat Ali Khan ◽  
Soyeon Caren Han ◽  
Jaehun Bang ◽  
...  
Keyword(s):  
Knowledge Acquisition ◽  
Knowledge Engineering ◽  
Data Driven ◽  
Acquisition System ◽  
Engineering Process ◽  
Production Rules ◽  
End To End

A data-drive model for the assessment of shallow landslides hazard with the integration of satellite soil moisture and rainfall data

2020 ◽  
Author(s):  
Valerio Vivaldi ◽  
Massimiliano Bordoni ◽  
Luca Lucchelli ◽  
Beatrice Corradini ◽  
Luca Brocca ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Methodological Approach ◽  
Return Time ◽  
Shallow Landslides ◽  
Predisposing Factors ◽  
Data Driven ◽  
Probability Of Occurrence ◽  
Triggering Factors ◽  
Triggering Events ◽  
Spatio Temporal

<p>Rainfall-induced shallow landslides are very dangerous phenomena, widespread all over the world, which could provoke significant damages to buildings, roads, facilities, cultivations and, sometimes, loss of human lives. For these reasons, it is necessary assessing the most prone zones in a territory which is particularly susceptible to these phenomena and the frequency of the triggering events, according to the return time of them, which generally correspond to intense and concentrated rainfalls. The most adopted methodologies for the determination of the susceptibility and hazard of a territory are physically-based models, that quantify the hydrological and the mechanical responses of the slopes according to particular rainfall scenarios. Whereas, these methodologies could be applied in a reliable way in little catchments, where geotechnical and hydrological features of the materials affected by shallow failures are homogeneous. Data-driven models could constraints these, even if they are generally built up taking into only the predisposing factors of shallow instabilities, allowing to estimate only the susceptibility of a territory, without considering the frequency of the triggering events. It is then required to consider also triggering factors of shallow landslides to allow these methods to estimate also the probability of occurrence and, then, the hazard. This work presents the development and the implementation of data-driven model able to assses the spatio-temporal probability of occurrence of shallow landslides in large areas by means of a data-driven technique. The model is based on Multivariate Adaptive Regression Technique (MARS), that links geomorphological, hydrological, geological and land use predisposing factors to triggering factors of shallow failures. These triggering factors correspond to soil saturation degree and rainfall amounts, which are available for entire a study area thanks to satellite measures. The methodological approach is testing in 30-40 km<sup>2</sup> wide catchments of Oltrepò Pavese hilly area (northern Italy), where detailed inventories of shallow landslides occurred during past triggering events and corresponding satellite soil moisture and rainfall maps are available. This work was made in the frame of the ANDROMEDA project, funded by Fondazione Cariplo.</p>

scholarly journals CAiRE: An End-to-End Empathetic Chatbot

2020 ◽  
Vol 34 (09) ◽  
pp. 13622-13623
Author(s):  
Zhaojiang Lin ◽  
Peng Xu ◽  
Genta Indra Winata ◽  
Farhad Bin Siddique ◽  
Zihan Liu ◽  
...  
Keyword(s):  
User Interface ◽  
Active Learning ◽  
Transfer Learning ◽  
User Feedback ◽  
Data Driven ◽  
Dialogue Systems ◽  
Generation Task ◽  
Web Based ◽  
End To End ◽  
Negative Training

We present CAiRE, an end-to-end generative empathetic chatbot designed to recognize user emotions and respond in an empathetic manner. Our system adapts the Generative Pre-trained Transformer (GPT) to empathetic response generation task via transfer learning. CAiRE is built primarily to focus on empathy integration in fully data-driven generative dialogue systems. We create a web-based user interface which allows multiple users to asynchronously chat with CAiRE. CAiRE also collects user feedback and continues to improve its response quality by discarding undesirable generations via active learning and negative training.

Using satellite soil moisture and rainfall data for the monitoring and the prediction of natural hazards

2021 ◽  
Author(s):  
Valerio Vivaldi ◽  
Massimiliano Bordoni ◽  
Luca Brocca ◽  
Luca Ciabatta ◽  
Claudia Meisina
Keyword(s):  
Methodological Approach ◽  
Shallow Landslides ◽  
Predisposing Factors ◽  
Data Driven ◽  
Probability Of Occurrence ◽  
Mechanical Responses ◽  
Triggering Factors ◽  
Physically Based ◽  
Adaptive Regression ◽  
Spatio Temporal

<p>Rainfall-induced shallow landslides affect buildings, roads, facilities, cultivations, causing several damages and, sometimes, loss of human lives. It is necessary assessing the most prone zones in a territory where these phenomena could occur and the triggering conditions of these events, which generally correspond to intense and concentrated rainfalls. The most adopted methodologies for the determination of the spatial and temporal probability of occurrence are physically-based models, that quantify the hydrological and the mechanical responses of the slopes according to particular rainfall scenarios. Whereas, they are limited to be applied in a reliable way in little catchments, where geotechnical and hydrological characteristics of the materials are homogeneous. Data-driven models could constraints these, when the predisposing factors of shallow instabilities, allowing to estimate only the susceptibility of a territory, are combined with triggering factors of shallow landslides to allow these methods to estimate also the probability of occurrence and, then, the hazard. This work presents the implementation of a data-driven model able to assses the spatio-temporal probability of occurrence of shallow landslides in large areas by means of a data-driven techniques. The models are based on Multivariate Adaptive Regression Technique (MARS), that links geomorphological, hydrological, geological and land use predisposing factors to triggering factors of shallow failures. These triggering factors correspond to soil saturation degree and rainfall amounts, which are available thanks to satellite measures (ASCAT and GPM). The methodological approach is testing in different catchments of Oltrepò Pavese hilly area (northern Italy), that is representative of Italian Apeninnes environment. This work was made in the frame of the project ANDROMEDA, funded by Fondazione Cariplo.</p>

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