Over the last forty years,many methodologies have been initiated within the framework of the participatory approach,the objective of which is to encourage the involvement of citizens in the definition and implementati...Over the last forty years,many methodologies have been initiated within the framework of the participatory approach,the objective of which is to encourage the involvement of citizens in the definition and implementation of projects and policies concerning them.The implementation of these participatory approaches in the field of interventional research in population health reveals several scientific,organizational,inter-individual,and ethical issues that must be discussed.Thus,we propose to present here the fruit of a collective reflection of the members of a research group,composed of patient-researchers and researchers in social psychology,on the implementation of the IMPAQT research project,which aimed to promote a community-based research approach in oncology.The discussion will be structured around three topics:the implementation of the participatory research mechanism,the sustainability of the commitment involved in participating in research,and the valorization of the participation of the patient-researchers.These issues are particularly important to consider in guiding the implementation of a solid and balanced partnership with those concerned in the co-construction of interventional research devices in cancerology.展开更多
A number of bridges have collapsed around the world over the past years,with detrimental consequences on safety and traffic.To a large extend,such failures can be prevented by regular bridge inspections and maintenanc...A number of bridges have collapsed around the world over the past years,with detrimental consequences on safety and traffic.To a large extend,such failures can be prevented by regular bridge inspections and maintenance,tasks that fall in the general category of structural health monitoring(SHM).Those procedures are time and labor consuming,which partly accounts for their neglect.Computer vision and artificial intelligence(AI)methods have the potential to ease this burden,by fully or partially automating bridge monitoring.A critical step in this automation is the identification of a bridge’s structural components.In this work,we propose an extensible synthetic dataset for structural component semantic segmentation of portal frame bridges(PFBridge).We first create a 3 dimensional(3D)generic mesh representing the bridge geometry,while respecting a set of rules.The definition of new,or the extension of the existing rules can adjust the dataset to specific needs.We then add textures and other realistic elements to the model,and create an automatically annotated synthetic dataset.The synthetic dataset is used in order to train a deep semantic segmentation model to identify bridge components on bridge images.The amount of available real images is not sufficient to entirely train such a model,but is used to refined the model trained on the synthetic data.We evaluate the contribution of the dataset to semantic segmentation by training several segmentation models on almost 2,000 synthetic images and then finetuning with 88 real images.The results show an increase of 28%on the F1-score when the synthetic dataset is used.To demonstrate a potential use case,the model is integrated in a 3D point cloud capturing system,producing an annotated point cloud where each point is associated with a semantic category(structural component).Such a point cloud can then be used in order to facilitate the generation of a bridge’s digital twin.展开更多
This article proposes a novel framework for the recognition of six universal facial expressions.The framework is based on three sets of features extracted from a face image:entropy,brightness,and local binary pattern....This article proposes a novel framework for the recognition of six universal facial expressions.The framework is based on three sets of features extracted from a face image:entropy,brightness,and local binary pattern.First, saliency maps are obtained using the state-of-the-art saliency detection algorithm "frequency-tuned salient region detection".The idea is to use saliency maps to determine appropriate weights or values for the extracted features (i.e.,brightness and entropy).We have performed a visual experiment to validate the performance of the saliency detection algorithm against the human visual system.Eye movements of 15 subjects were recorded using an eye-tracker in free-viewing conditions while they watched a collection of 54 videos selected from the Cohn-Kanade facial expression database.The results of the visual experiment demonstrated that the obtained saliency maps are consistent with the data on human fixations.Finally,the performance of the proposed framework is demonstrated via satisfactory classification results achieved with the Cohn-Kanade database,FG-NET FEED database, and Dartmouth database of children's faces.展开更多
Smart learning systems provide relevant learning resources as a personalized bespoke package for learners based on their pedagogical needs and individual preferences.This paper introduces a learning style model to rep...Smart learning systems provide relevant learning resources as a personalized bespoke package for learners based on their pedagogical needs and individual preferences.This paper introduces a learning style model to represent features of online learners.It also presents an enhanced recommendation method named Adaptive Recommendation based on Online Learning Style(AROLS),which implements learning resource adaptation by mining learners’behavioral data.First,AROLS creates learner clusters according to their online learning styles.Second,it applies Collaborative Filtering(CF)and association rule mining to extract the preferences and behavioral patterns of each cluster.Finally,it generates a personalized recommendation set of variable size.A real-world dataset is employed for some experiments.Results show that our online learning style model is conducive to the learners’data mining,and AROLS evidently outperforms the traditional CF method.展开更多
Context modelling involves a） characterizing a situation with related information, and b） dealing and stor- ing the information in a computer-understandable form. It is the keystone to enable a system to possess the...Context modelling involves a） characterizing a situation with related information, and b） dealing and stor- ing the information in a computer-understandable form. It is the keystone to enable a system to possess the perception ca- pacity and adapt its functionality properly for different situa- tions. However, a context model focusing on the characteris- tics of work-based learning is not well studied by pioneering researchers. For addressing this issue, in this work we firstly analyze several existing context models to identify the essen- tials of context modelling, whereby a hierarchical ontology context model is proposed to characterize work-based learn- ing. Subsequently, we present the application of the proposed model in work-based learning scenario to provide adapted learning supports to professionals. Hence, this work has sig- nificance in both theory and practice.展开更多
City models have a wide variety of uses that require different kind of data representation or data models.Having a dynamic model that enables picking the right representations(meshes,volumetric data,point cloud,etc.)c...City models have a wide variety of uses that require different kind of data representation or data models.Having a dynamic model that enables picking the right representations(meshes,volumetric data,point cloud,etc.)can prove useful to adapt an application to each user’s needs.In this paper,we present an original method to create personalised visualisations of 3D city models on the fly.By organising the server data in a hierarchy of tiles,we are able to generate personalised models based on the user’s preferences.These preferences take the shape of a set of rules that apply to each tile or city object and allow the user to choose which representation of the object to use depending on its position or semantic information(classification,height,etc.).Our method is designed around existing standards,guaranteeing the interoperability of the produced models.展开更多
文摘Over the last forty years,many methodologies have been initiated within the framework of the participatory approach,the objective of which is to encourage the involvement of citizens in the definition and implementation of projects and policies concerning them.The implementation of these participatory approaches in the field of interventional research in population health reveals several scientific,organizational,inter-individual,and ethical issues that must be discussed.Thus,we propose to present here the fruit of a collective reflection of the members of a research group,composed of patient-researchers and researchers in social psychology,on the implementation of the IMPAQT research project,which aimed to promote a community-based research approach in oncology.The discussion will be structured around three topics:the implementation of the participatory research mechanism,the sustainability of the commitment involved in participating in research,and the valorization of the participation of the patient-researchers.These issues are particularly important to consider in guiding the implementation of a solid and balanced partnership with those concerned in the co-construction of interventional research devices in cancerology.
基金The project MIRAUAR received a Grant from the call for projects Ponts Connectés funded by the French State and led by Cerema which aims to improve bridge management by using the most recent techniques in terms of monitoring,data transfer and data processing.
文摘A number of bridges have collapsed around the world over the past years,with detrimental consequences on safety and traffic.To a large extend,such failures can be prevented by regular bridge inspections and maintenance,tasks that fall in the general category of structural health monitoring(SHM).Those procedures are time and labor consuming,which partly accounts for their neglect.Computer vision and artificial intelligence(AI)methods have the potential to ease this burden,by fully or partially automating bridge monitoring.A critical step in this automation is the identification of a bridge’s structural components.In this work,we propose an extensible synthetic dataset for structural component semantic segmentation of portal frame bridges(PFBridge).We first create a 3 dimensional(3D)generic mesh representing the bridge geometry,while respecting a set of rules.The definition of new,or the extension of the existing rules can adjust the dataset to specific needs.We then add textures and other realistic elements to the model,and create an automatically annotated synthetic dataset.The synthetic dataset is used in order to train a deep semantic segmentation model to identify bridge components on bridge images.The amount of available real images is not sufficient to entirely train such a model,but is used to refined the model trained on the synthetic data.We evaluate the contribution of the dataset to semantic segmentation by training several segmentation models on almost 2,000 synthetic images and then finetuning with 88 real images.The results show an increase of 28%on the F1-score when the synthetic dataset is used.To demonstrate a potential use case,the model is integrated in a 3D point cloud capturing system,producing an annotated point cloud where each point is associated with a semantic category(structural component).Such a point cloud can then be used in order to facilitate the generation of a bridge’s digital twin.
文摘This article proposes a novel framework for the recognition of six universal facial expressions.The framework is based on three sets of features extracted from a face image:entropy,brightness,and local binary pattern.First, saliency maps are obtained using the state-of-the-art saliency detection algorithm "frequency-tuned salient region detection".The idea is to use saliency maps to determine appropriate weights or values for the extracted features (i.e.,brightness and entropy).We have performed a visual experiment to validate the performance of the saliency detection algorithm against the human visual system.Eye movements of 15 subjects were recorded using an eye-tracker in free-viewing conditions while they watched a collection of 54 videos selected from the Cohn-Kanade facial expression database.The results of the visual experiment demonstrated that the obtained saliency maps are consistent with the data on human fixations.Finally,the performance of the proposed framework is demonstrated via satisfactory classification results achieved with the Cohn-Kanade database,FG-NET FEED database, and Dartmouth database of children's faces.
基金supported by the National Natural Science Foundation of China (No. 61977003),entitled “Research on learning style for adaptive learning: modelling, identification and applications”
文摘Smart learning systems provide relevant learning resources as a personalized bespoke package for learners based on their pedagogical needs and individual preferences.This paper introduces a learning style model to represent features of online learners.It also presents an enhanced recommendation method named Adaptive Recommendation based on Online Learning Style(AROLS),which implements learning resource adaptation by mining learners’behavioral data.First,AROLS creates learner clusters according to their online learning styles.Second,it applies Collaborative Filtering(CF)and association rule mining to extract the preferences and behavioral patterns of each cluster.Finally,it generates a personalized recommendation set of variable size.A real-world dataset is employed for some experiments.Results show that our online learning style model is conducive to the learners’data mining,and AROLS evidently outperforms the traditional CF method.
文摘Context modelling involves a） characterizing a situation with related information, and b） dealing and stor- ing the information in a computer-understandable form. It is the keystone to enable a system to possess the perception ca- pacity and adapt its functionality properly for different situa- tions. However, a context model focusing on the characteris- tics of work-based learning is not well studied by pioneering researchers. For addressing this issue, in this work we firstly analyze several existing context models to identify the essen- tials of context modelling, whereby a hierarchical ontology context model is proposed to characterize work-based learn- ing. Subsequently, we present the application of the proposed model in work-based learning scenario to provide adapted learning supports to professionals. Hence, this work has sig- nificance in both theory and practice.
基金This work has been supported by the French company Oslandia through the phd thesis of Jérémy Gaillard.CityGML data are provided by Lyon metropole and Helsinki Region Infoshare.This research was partly supported by the French Council for Technical Research(ANRT).
文摘City models have a wide variety of uses that require different kind of data representation or data models.Having a dynamic model that enables picking the right representations(meshes,volumetric data,point cloud,etc.)can prove useful to adapt an application to each user’s needs.In this paper,we present an original method to create personalised visualisations of 3D city models on the fly.By organising the server data in a hierarchy of tiles,we are able to generate personalised models based on the user’s preferences.These preferences take the shape of a set of rules that apply to each tile or city object and allow the user to choose which representation of the object to use depending on its position or semantic information(classification,height,etc.).Our method is designed around existing standards,guaranteeing the interoperability of the produced models.
