SciTePress - Affiliation Profile

LIRIS-CNRS, France

Context (please use complete words only)

Field

Sort by

15 Papers

Toward a Meta-design Method for Learning Games

Topics: Authoring Tools and Content Development; Game-Based and Simulation-Based Learning; Tools for Educational Communication and Collaboration

Authors: Marne Bertrand ¹ , Muratet Mathieu ^{2

;

3} and Sehaba Karim ⁴

Affiliations: ¹ ICAR UMR 5191, Université Lumière Lyon 2, France , ² INS HEA, 92150 Suresnes, France , ³ Sorbonne Université, CNRS, LIP6, F-75005 Paris, France , ⁴ Université de Lyon, CNRS. Université Lyon 2, LIRIS, UMR5205, F-69676, France

Digital Object Identifier: 10.5220/0010530203700376

Proceedings of CSEDU 2021, ISBN: 978-989-758-502-9

Quick Abstract

To support the appropriation of serious learning games by teachers, we are studying design methods that focus on both design and use phases: meta-design methods. Our objective is to propose models and tools for designing levels and scenarios for Blockly Maze and to provide the teacher with monitoring indicators allowing him/her to appropriate and adapt the game according to the observed uses. In this article, we detail the first contributions of this study based on qualitative data: analysis of the game Blockly Maze and interviews with two teachers. First results draw three main needs: level design tool, scenario design tool and monitoring tool. Beyond these needs we introduce underlying models of each of them and future works.

CAD-based Learning for Egocentric Object Detection in Industrial Context

Topics: Object Detection and Localization; Mobile and Egocentric Object Detection and Recognition; First Person Vision

Authors: Julia Cohen ¹ , Carlos Crispim-Junior ² , Céline Grange-Faivre ³ and Laure Tougne ²

Affiliations: ¹ Univ. Lyon, Lyon 2, LIRIS, F-69676 Lyon, France, DEMS, Saint Bonnet de Mûre, France , ² Univ. Lyon, Lyon 2, LIRIS, F-69676 Lyon, France , ³ DEMS, Saint Bonnet de Mûre, France

Digital Object Identifier: 10.5220/0008975506440651

Proceedings of VISAPP 2020, ISBN: 978-989-758-402-2

Quick Abstract

Industries nowadays have an increasing need of real-time and accurate vision-based algorithms. Although the performance of object detection methods improved a lot thanks to massive public datasets, instance detection in industrial context must be approached differently, since annotated images are usually unavailable or rare. In addition, when the video stream comes from a head-mounted camera, we observe a lot of movements and blurred frames altering the image content. For this purpose, we propose a framework to generate a dataset of egocentric synthetic images using only CAD models of the objects of interest. To evaluate different strategies exploiting synthetic and real images, we train a Convolutional Neural Network (CNN) for the task of object detection in egocentric images. Results show that training a CNN on synthetic images that reproduce the characteristics of egocentric vision may perform as well as training on a set of real images, reducing, if not removing, the need to manually annotate a large quantity of images to achieve an accurate performance.

Learner Performance Prediction Indicators based on Machine Learning

Topics: Machine Learning; Learning Analytics; Educational Data Mining

Author: Karim Sehaba

Affiliation: Université de Lyon, CNRS. Université Lyon 2, LIRIS, UMR5205, F-69676, France

Digital Object Identifier: 10.5220/0009396100470057

Proceedings of CSEDU 2020, ISBN: 978-989-758-417-6

Quick Abstract

This work is interested in the analysis of learners’ performances in order to define indicators to predict their results based on their interactions with a learning environment. These indicators should alert learners at risk, or their teachers, by highlighting their difficulties in order to help them get around them before it is too late. For this, we have defined a trace analysis approach based on the use of machine learning methods. This approach consists of preparing the plotted data automatically and manually, by selecting the attributes relevant to learning, then automatically extracting indicators explaining the learner’s results. Our work was applied to a data set resulting from a real training comprising 32593 learners producing 10 655 280 events. The accuracy of our predictions has reached around 80%. Rules extraction methods were also applied in order to explain the rules which govern the prediction indicator.

Efficient Bark Recognition in the Wild

Topics: Color and Texture Analyses; Features Extraction

Authors: Rémi Ratajczak ¹ , Sarah Bertrand ² , Carlos Crispim-Junior ² and Laure Tougne ²

Affiliations: ¹ Univ Lyon, Lyon 2, LIRIS, F-69676 Lyon, France, Unité Cancer et Environnement, Centre Léon Bérard, Lyon, France, Agence de l’Environnement et de la Maítrise de l’Energie, Angers and France , ² Univ Lyon, Lyon 2, LIRIS, F-69676 Lyon and France

Digital Object Identifier: 10.5220/0007361902400248

Proceedings of VISAPP 2019, ISBN: 978-989-758-354-4

Quick Abstract

In this study, we propose to address the difficult task of bark recognition in the wild using computationally efficient and compact feature vectors. We introduce two novel generic methods to significantly reduce the dimensions of existing texture and color histograms with few losses in accuracy. Specifically, we propose a straightforward yet efficient way to compute Late Statistics from texture histograms and an approach to iteratively quantify the color space based on domain priors. We further combine the reduced histograms in a late fusion manner to benefit from both texture and color cues. Results outperform state-of-the-art methods by a large margin on four public datasets respectively composed of 6 bark classes (BarkTex, NewBarkTex), 11 bark classes (AFF) and 12 bark classes (Trunk12). In addition to these experiments, we propose a baseline study on Bark-101, a new challenging dataset including manually segmented images of 101 bark classes that we release publicly.

Related Ontology Subjects/Areas/Topics (4)

Color and Texture Analyses		Features Extraction		Image and Video Analysis
Computer Vision, Visualization and Computer Graphics

Generation of Task Models from Observed Usage Application to Web Browsing Assistance

Topics: Communication, Collaboration and Information Sharing; Tools and Technology for Knowledge Management; Knowledge Management Projects; Intelligent Information Systems; Information Technology and Information System Research

Authors: Karim Sehaba ¹ and Benoît Encelle ²

Affiliations: ¹ Université de Lyon, CNRS, Université de Lyon 2, LIRIS, UMR5205 and France , ² Université de Lyon, CNRS, Université de Lyon 1, LIRIS, UMR5205 and France

Digital Object Identifier: 10.5220/0008068300740082

Proceedings of KMIS 2019, ISBN: 978-989-758-382-7

Quick Abstract

This work focuses on the extraction of knowledge from observed usage. More specifically, it aims to design a Web browsing assistance system that helps the user in carrying out his browsing task, or the designer in adapting or redesigning his Web application, according to real usage. The proposed approach consists of generating task models from interaction traces, which are then used to perform assistance. The characteristics to be supported by a task metamodel for assistance purposes are first identified and then confronted with the characteristics of existing task metamodels. This first study led us to choose the ConcurTaskTrees (CTT) metamodel. We then developed processes to generate CTT task models from traces. Finally, to validate our approach, we conducted unit testing and validation based on two real web browsing scenarios.

Related Ontology Subjects/Areas/Topics (8)

Artificial Intelligence	Knowledge Management and Information Sharing	Symbolic Systems
Communication, Collaboration and Information Sharing	Knowledge Management Projects	Tools and Technology for Knowledge Management
Intelligent Information Systems	Knowledge-Based Systems

Analysis of Serious Games based Learning Requirements using Feedback and Traces of Users

Topics: e-Learning Platforms, Portals; Distance and E-Learning in a Global Context; Distance Education; Game-Based and Simulation-Based Learning; Gamification for Learning

Authors: Afef Ghannem ¹ , Karim Sehaba ² , Raoudha Khcherif ¹ and Henda Ben Ghezala ¹

Affiliations: ¹ Université de la Manouba, Tunisia , ² Université Lyon 2, France

Proceedings of CSEDU 2018, ISBN: 978-989-758-291-2

Quick Abstract

Identify the games that best meet the needs and expectations of teachers and objectives of their courses remains a necessity about the integration of serious games among active teaching methods. Indeed, several serious games have developed in recent years, and it is often difficult for a teacher, not a computer scientist in particular, to find a game that meets these specific needs. Our aim is to develop models and tools enabling the teacher to find serious games adapted to his needs, considering user feedback and their traces of interaction with the game. To this end, we have explored the evaluation methods of serious games as well as methods of extracting knowledge from traces and texts. In this paper, we present our method of knowledge extraction of educational objectives. Thus, our proposal is assisting and supporting teachers/trainers to choose serious games and easily integrate them into their learning processes and devices.

Related Ontology Subjects/Areas/Topics (11)

Computer-Supported Education	e-Learning Platforms	Simulation Tools and Platforms
Distance and e-Learning in a Global Context	Game-Based and Simulation-Based Learning	Social Context and Learning Environments
Distance Education	Learning/Teaching Methodologies and Assessment	Ubiquitous Learning
e-Learning	Simulation and Modeling

Meshing Meristems - An Iterative Mesh Optimization Method for Modeling Plant Tissue at Cell Resolution

Topics: Image Processing Methods; Confocal Microscopy

Authors: Guillaume Cerutti and Christophe Godin

Affiliation: INRIA, France

Digital Object Identifier: 10.5220/0005190100230035

Proceedings of BIOIMAGING 2015/BIOSTEC, ISBN: 978-989-758-072-7

Quick Abstract

We address in this paper the problem of reconstructing a mesh representation of plant cells in a complex, multi-layered tissue structure, based on segmented images obtained from confocal microscopy of shoot apical meristem of model plant Arabidopsis thaliana. The construction of such mesh structures for plant tissues is currently a missing step in the existing image analysis pipelines. We propose a method for optimizing the surface triangular meshes representing the tissue simultaneously along several criteria, based on an initial low-quality mesh. The mesh geometry is deformed by iteratively minimizing an energy functional defined over this discrete surface representation. This optimization results in a light discrete representation of the cell surfaces that enables fast visualization, and quantitative analysis, and gives way to in silico physical and mechanical simulations on real-world data. We provide a framework for evaluating the quality of the cell tissue reconstruction, that underlines the ability of our method to fit multiple optimization criteria.

Related Ontology Subjects/Areas/Topics (4)

Bioimaging		Confocal Microscopy		Image Processing Methods
Biomedical Engineering

Recommendation of Learning Resources based on Social Relations

Topics: e-Learning Platforms, Portals; Collaborative Learning; Theoretical Bases of E-Learning Environments

Authors: Mohammed Tadlaoui ¹ , Karim Sehaba ² and Sébastien George ³

Affiliations: ¹ Université de Tlemcen and INSA de Lyon, Algeria , ² Université de Lyon, France , ³ LUNAM Université and Université du Maine, France

Digital Object Identifier: 10.5220/0005452304250432

Proceedings of CSEDU 2015, ISBN: 978-989-758-108-3

Quick Abstract

Recommender systems are able to estimate the interest for a user of a given resource from some information about similar users and resources properties. In our work, we focus on the recommendations of educational resources in the field of Technology Enhanced Learning (TEL) and more specifically the recommendations which are based on social information. Based on the results of research in recommender systems and TEL, we define an approach to recommend learning resources using social information present in social networks. We have developed a formal model for the calculation of similarity between users and the generation of three types of recommendation. We also developed a platform that implements our approach.

Related Ontology Subjects/Areas/Topics (8)

Collaborative Learning	e-Learning Platforms	Social Context and Learning Environments
Computer-Supported Education	Simulation and Modeling	Theoretical Bases of e-Learning Environments
e-Learning	Simulation Tools and Platforms

About the Impact of Pre-processing Tools on Segmentation Methods - Applied for Tree Leaves Extraction

Topics: Segmentation and Grouping; Features Extraction

Authors: Manuel Grand-Brochier ¹ , Antoine Vacavant ² , Robin Strand ³ , Guillaume Cerutti ⁴ and Laure Tougne ⁴

Affiliations: ¹ University Lumiere Lyon 2, France , ² University d’Auvergne, France , ³ Uppsala University, Sweden , ⁴ LIRIS UMR5205 CNRS, France

Digital Object Identifier: 10.5220/0004690405070514

Proceedings of VISAPP 2014/VISIGRAPP, ISBN: 978-989-758-003-1

Quick Abstract

In this paper, we present a comparative study highlighting the improvements provided by pre-processing tools, such as input stroke or use of distance map for segmentation approaches. We propose in particular to highlight new methods for calculating distance map based on the prediction of changes in local color (published by G. Cerutti et al. in ReVeS Participation - Tree Species Classification Using Random Forests and Botanical Features. CLEF 2012). We study differents methods using thresholding, clustering, or even active contours, tested for an issue of tree leaves extraction. The observation criteria, such as Dice index, SSIM or MAD for example, allow us to analyze the performance obtained by each approach and in particular those of the GAC method, which are better for this context.

Related Ontology Subjects/Areas/Topics (4)

Computer Vision, Visualization and Computer Graphics		Image and Video Analysis		Segmentation and Grouping
Features Extraction

Capitalize and Share Observation and Analysis Knowledge to Assist Trainers in Professional Training with Simulation - Case of Training and Skills Maintain of Nuclear Power Plant Control Room Staff

Topics: Educating the Educators; Industrial, Financial and Medical Applications

Authors: Olivier Champalle ¹ , Karim Sehaba ² and Alain Mille ¹

Affiliations: ¹ Universite de Lyon and Universite de Lyon 1, France , ² Universite de Lyon and Universite de Lyon 2, France

Digital Object Identifier: 10.5220/0004388806270632

Proceedings of CSEDU 2013, ISBN: 978-989-8565-53-2

Quick Abstract

The observation and analysis of the activity of learners in computerized environments training is a major issue, particularly in the context of professional training on nuclear power plant full-scope simulator. In such a context, the role of the trainers is critic and require constant alertness throughout the simulation especially for the young trainers. The objective of our work is to propose an approach to facilitate the observation and analysis of the trainees’ activities. This approach is based on interaction traces. It consists in representing the operators’ actions and the simulation data in the form of modelled traces. These modelled traces are transformed in order to extract higher informations levels on the behaviour of trainees. Trainers can visualize the different levels to analyse the reasons, of successes or failure of trainees. This approach has been implemented in a prototype, called D3KODE, allowing the representation, processing and visualization of traces. D3KODE was evaluate according to a comparative protocol conducted with a team of trainers from EDF Group.

Related Ontology Subjects/Areas/Topics (22)

Artificial Intelligence	Health Engineering and Technology Applications	Neurotechnology, Electronics and Informatics
Biomedical Engineering	Human-Computer Interaction	Pattern Recognition
Biomedical Signal Processing	Industrial, Financial and Medical Applications	Physiological Computing Systems
Computational Intelligence	Learning/Teaching Methodologies and Assessment	Sensor Networks
Computer-Supported Education	Methodologies and Methods	Signal Processing
Domain Applications and Case Studies	Neural Networks	Soft Computing
Educating the Educators	Neurocomputing	Theory and Methods
Fuzzy Systems

Topic(s):

the modern design of rolex replica is great.

best new swiss replica uhren online.

Affiliation

Filter listed results, by

Title:
ISBN:
Year:
Acronym:
Subject:

Title:
Author:
Affiliation:
Subject:

Name:
Affiliation:
Country:
Conference:
Subject:

Name:
Country:
Subject: