Archives

Stage Deep Learning sur Graphes (GNN, Transformers)

Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : LIFAT Tours France
Durée : 5 mois
Contact : jyramel@univ-tours.fr
Date limite de publication : 2022-01-31

Contexte :
Ce stage est proposé dans le cadre d’un projet collaboratif mené par des membres du LIFAT (à Tours) et du LITIS (à Rouen) et visant à mieux coupler les techniques d’apprentissage profond et de reconnaissance des formes structurelles (traitement de données de type graphes, geometric deep learningn GNN, Graph Transormer).

Sujet :
Missions du stage :
Intégration d’informations structurelles dans le mécanisme d’attention des Graph Transformers ou GNN.
Dans des travaux précédents [2, 3], une première architecture GNN travaillant directement dans l’espace des graphes a été proposée. Les opérateurs de convolution et de pooling sont définis dans le domaine des graphes tout en permettant l’utilisation d’un algorithme de rétro-propagation pendant l’étape d’apprentissage. En particulier, la convolution est remplacée par un solveur d’appariement de graphes [5] appliqué sur un sous-graphe enraciné autour de chaque nœud du graphe. L’idée est d’étudier l’utilisation d’un solveur de mise en correspondance de graphes dans l’objectif d’un mécanisme d’attention structurelle. L’objectif de ce travail serait ainsi de :
1. Etudier des méthodes alternatives de mise en place du mécanisme d’attention pour prendre mieux en compte les informations structurelles.
2. Proposer un modèle de transformer de graphes basé sur un de ces mécanismes d’attention structurel.
3. Programmer ces modèles (en Python), et les comparer à l’état de l’art sur des jeux de données standards pour différentes applications.

Code suggéré : Les lecteurs intéressés pourraient considérer le code suivant comme une base de référence: https://github.com/graphdeeplearning/graphtransformer

Profil du candidat :
• Licence/master en informatique, mathématiques appliquées, science des données, ou similaire.
• Compétences (avec expériences si possible) : réseaux neuronaux, apprentissage profond, programmation Python, analyse numérique.

Le stage se déroulera entre fevrier et septembre 2022.
Possibilité de poursuite en thèse en septembre 2022

Formation et compétences requises :
• Licence/master en informatique, mathématiques appliquées, science des données, ou similaire.
• Compétences (avec expériences si possible) : réseaux neuronaux, apprentissage profond, programmation Python, analyse numérique.

Adresse d’emploi :
Le stage aura lieu au Laboratoire d’Informatique Fondamentale et Appliquees de Tours (LIFAT, http://lifat.univ-tours.fr )

Veuillez soumettre votre CV en format pdf à: ramel@univ-tours.fr and romain.raveaux@univ-tours.fr.

Document attaché : 202111181415_LIFAT_Internship_ANR_CodeGNNen.pdf

Combining educational resources through graph representation learning

Offre en lien avec l’Action/le Réseau : – — –/Doctorants

Laboratoire/Entreprise : IRISA / Université de Rennes 1
Durée : 3ans
Contact : zoltan.miklos@irisa.fr
Date limite de publication : 2022-01-31

Contexte :
There is a large number of publicly available learning resources. Combining these resources and creating potential coherent sequences to achieve a specific learning goal is a challenging task for educators. Identifying resources to complete or complement an existing course also requires a considerable effort. At the same time, teachers and professors are confronted with the task of creating online courses within a very short time, in particular during the Covid sanitary crisis. Making sense of large collections and especially identifying connections between learning resources is challenging and time-consuming.

Sujet :
The objective of the CLARA project, financed by Cominlabs, is to support and assist educators in associating learning resources to learning paths, in particular relative to the designed curricula. We would like to design such methods with the help of various methods from artificial intelligence. Specifically, we will associate various pieces of information to the resources, such as metadata and knowledge graphs. Then we would like to exploit graph matching and graph representation learning [Ham2020] techniques that relate these individual graphs and identify more specific connections between the ressources.
However, the graph representation learning methods are not directly adapted to address the specific problem of linking open educational resources, for the following reasons :
There could be several knowledge graphs that are associated with a specific resource. We could also have different versions of the same knowledge graph.
Besides the knowledge graphs, we can have other metadata that could be exploited.
Most importantly, if we would like to complete an existing path of resources with an additional one, the choice might depend on the entire path and not only one single resource of this path. In other words, in order to predict which resources are related and could be used in a curriculum, we should exploit higher-order features [Bick2021] of the networks and tensors that we will construct. The learned graph representation should also represent the paths of resources.

We propose to work on this specific problem in the thesis. We plan to develop representation learning techniques for higher-order networks that can support path finding methods. There are some recent works in this direction, including [Rossi18], [Saebi21] and [Benson2018]. However, these works do not focus on knowledge graphs. Moreover, prerequisite relations between concepts, if they are known, should also be given special attention.

Profil du candidat :
– Master in computer science
– with good results
– interest in research,
– scientific curiosity

Formation et compétences requises :
– machine learning
– graph representation learning
– knowledge graphs
– programming in Python
– very good command of English
– French is a plus, but not required

Adresse d’emploi :
Univ Rennes CNRS IRISA
Campus universitaire de Beaulieu
263 av Gen Leclerc
35024 Rennes cedex
France

Document attaché : 202111180821_2021 PhD position at IRISA.pdf

Postdoctoral Researcher in Deep Learning for Video Analysis

Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : ImViA
Durée : 12 – 24 months
Contact : yannick.benezeth@u-bourgogne.fr
Date limite de publication : 2022-02-17

Contexte :
The ImViA lab at University Burgundy (Dijon – France), together with Honda Research Institute (Japan), invites applications for postdoctoral research positions in deep learning for video analysis.

Interested candidates should submit their CV, letter(s) of reference, and a brief research statement describing their background and research interests and how they align with the project emailed to Yannick Benezeth (yannick.benezeth@u-bourgogne.fr). The call will remain open until the position is filled. The postdoc contract will start as soon as possible.

Sujet :
The postdoctoral researcher will work on improving existing deep learning models to estimate physiological signals from the video signal. Remote photoplethysmography (rPPG) is a recent technique for estimating heart rate and other vital signs by analyzing subtle skin color variations using regular cameras (see [1] for an interesting review). More recently, end-to-end approaches based on deep learning have also been used. We will seek to extend existing work by improving current models, focusing on night vision applications. The candidate will take part in ongoing projects and possibly initiate new research within the team.

[1] Rouast, P. V., Adam, M. T., Chiong, R., Cornforth, D., & Lux, E. (2018). Remote heart rate measurement using low-cost RGB face video: a technical literature review. Frontiers of Computer Science, 12(5), 858-872.

Profil du candidat :
The postdoctoral researcher will work in Dijon – France in collaboration with researchers from the Honda Research Institute in Japan. This fellowship has a duration of 12 months with possibility of extension. As part of this postdoc, we can offer generous support for professional travel and research needs.

We are seeking a highly qualified and motivated candidate with a Ph.D. in Computer Vision, Machine Learning, Image processing, Biomedical Engineering, or a closely related field with a relevant scientific track record on significant computer vision conferences/journals as well as experience on deep learning techniques and frameworks.

Formation et compétences requises :
Ph.D. in Computer Vision, Machine Learning, Image processing, Biomedical Engineering

Adresse d’emploi :
The postdoctoral researcher will work in Dijon – France.

ACDC with deep learning : Automatic Crater Detection and Characterization

Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : Université Paris-Saclay, Centrale SUPELEC
Durée : 4-6 months + 3 years
Contact : frederic.schmidt@universite-paris-saclay.fr
Date limite de publication : 2022-04-29

Contexte :
This study takes place in the data deluge from the numerous space missions across the Solar System. The project proposes to develop a tool to automatically detect and characterize the most ubiquitous feature on planetary body : craters.
The aim is to developed a tool to define precise size and position of all craters in the scene, whatever the illumination conditions, the type of sensor and the scale. As a second goal, the project will have to determine the crater characteristics, such primary / secondary (ejecta from a previous impact, not from a direct impactor), presence / absence of rays, erosion level…

Sujet :
This study will take advantage of the machine learning and deep learning libraries available as open source to propose the most versatile and robust detection method. We propose to develop a new tool dedicated to this task. Such software pipeline is required to tackle fundamental questions in planetary science to study the surface processes across the Solar System. It will be a crucial tool to precisely date the surface and open a new era for onboard decisions on landing or targeting, to maximize the science return of future deep space missions.

The internship subject should continue in PhD and will take place in collaboration between planetary scientist and IA expert within University Paris-Saclay/Centrale SUPELEC.

Profil du candidat :
The candidate must have a engineer or master grade in machine learning/data mining or in astronomy/planetary science. Double competence in both fields will be encouraged.

Formation et compétences requises :
An excellent level of programming skills is required (Python, Linux). We expect the candidate to have a good level of communication in English (written and oral).

Adresse d’emploi :
Campus Université Paris-Saclay
91400 ORSAY, FRANCE

Post doctoral position on time series analysis for the neural caracterisation of different levels of

Offre en lien avec l’Action/le Réseau : RoCED/– — –

Laboratoire/Entreprise : GREYC UMR 6072
Durée : One year
Contact : luc.brun@ensicaen.fr
Date limite de publication : 2022-04-29

Contexte :
We are seeking an outstanding postdoctoral research fellow with experience in deep learning / machine learning to work with us at Caen University, France on a project investigating the analysis of multimodal time series for the characterization of brain functional connectivity in different levels of wakefulness.

The postdoctoral position is funded under the research project LOR supported by the Region Normandy (France). The LOR project gathers engineering schools and universities.

Sujet :
Background

Brain activity can be recorded in humans either by techniques based on the
metabolic functioning of the neuron, such as Positron Emission Tomography (PET) or Magnetic Resonance Imaging (MRI), or by techniques based on the electrical functioning of the neuron, such as electroencephalography (EEG) or magnetoencephalography (MEG). If the first type of measurement allows to obtain recordings with an interesting spatial resolution, the second type allows a higher temporal resolution. To summarize, these two approaches are both imperfect but complementary.

In this project, we are interested by this double approach in the context of
the human characterization of different levels of wakefulness (from full awake to deep sleep). Data in animals indicate that the transition from wakefulness to sleep causes changes in the relationships between different brain structures. Sensory inputs via the thalamus are inhibited leading to a decrease in thalamo-cortical links in favor of intra-cortical relations. These modifications progressively isolate the cortex in order to facilitate the descent into sleep. In terms of connectivity, these modifications are reflected in EEG by a clear decrease in global long-distance connections which are progressively replaced by an intensification of local cortico-cortical connectivity [1]. On the other hand, MRI connectivity analyses tend to show that the spatial extent of the networks is preserved during the early phases of sleep [2]. It is necessary to explore this apparent paradox in order to better understand the cerebral mechanisms at play during the descent into sleep but also more broadly to explore the networks of human consciousness. . .

Objectives and challenges

The project is based on data from a cohort being currently acquired. It includes EEG and MRI acquisitions performed while the subjects are falling asleep for a nap. In a first step, the candidate will study the dynamic evolution of the functional connectivity measured in EEG as a function of the correlation metric (spectral coherence, synchronization probability, phase synchronization method, etc.). In a second step, these results will have to be compared to those obtained in MRI by taking into account the physical characteristics of the different signals.

Work plan

In both cases (EEG/MRI) the correlations between the different areas will be
measured using positive defined matrices measuring the correlation of the signals. For the EEG, these correlations can be measured directly from the temporal signals or from time-frequency analyses. In a first step, and for the EEG, we will have to characterize the matrices corresponding to the different phases of sleep using the calculation of averages on the variety of positive defined matrices [3]. In a second step, we will try (in EEG as well as in fMRI) to design recurrent networks on such matrices [4] in order to automatically classify the sleep phases as the acquisition progresses.

Profil du candidat :
* Interpersonal skills and the ability to work well individually or as a member of a project team are recommended.
* Good written and verbal communication skills are required, the candidate
has to be fluent in spoken French or English and written English. Working
language can be English or French.

Formation et compétences requises :
* The candidate must have a recent Ph.D. (within 5 years) in Computer
Science (or Applied Mathematics) in the field of Machine Learning.
* Knowledge and experience within Deep Learning frameworks is highly
recommended.
* The candidate will perform research and algorithmic developments and
solid programming skills are required.

Adresse d’emploi :
Interested candidates should submit their application to

• luc.brun@ensicaen.fr and
• olivier.etard@unicaen.fr

Please include in your application email one Curriculum Vitae, one statement of research letter explaining your interest and your skills for this position, and 2 reference letters (all in a single pdf file). Applications will be admitted until the position is filled.

Document attaché : 202111161052_postdoc_en.pdf

ImaginEcology@Alpes : image, écologie et machine learning pour l’étude de la faune sauvage des Alpes

Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : CNRS Lyon/Grenoble/Chambéry
Durée : 5 à 6 mois
Contact : vincent.miele@univ-lyon1.fr
Date limite de publication : 2022-02-15

Contexte :
Depuis plusieurs années, de nombreux acteurs de l’étude de la faune sauvage (parcs et réserves naturelles, office français de la biodiversité, chercheur.ses) ont installés des centaines de “pièges photographiques” en France, boitiers à déclenchement automatique qui sont censés photographier les animaux durant leur passage. En particulier, des dizaines de ses appareils ont été installés dans le cadre de l’observatoire ORCHAMP de la Zône Atelier Alpes, pilotée par W.Thuiller (co-encadrant). Ces matériels produisent une “avalanche” de données photographiques qu’il faut trier a posteriori : trier les images vides et identifier les espèces.
Dans le même temps, une équipe CNRS incluant des membres du LECA et du LBBE et pilotée par V.Miele (co-encadrant), s’est mobilisée pour proposer une application de vision par ordinateur qui permettrait l’identification automatique des espèces de la faune sauvage française dans les images issues de pièges photos (cf. image de renard ci-dessus).

Les dernières techniques de deep learning sont appliquées, à partir d’une banque de données de plus de 500 000 images annotées de la faune française (renard, loup, cerf, chamois,…). Un prototype d’application Tensorflow-Keras/Python est d’ores et déjà en phase de test.

Sujet :
La mise au point de la chaîne de traitement des images de l’observatoire ORCHAMP reste à mettre en œuvre, avec pour objectif la capacité à analyser end-to-end les centaines de milliers d’images de la faune arrivant en flux régulier depuis le terrain.

Le/la stagiaire s’attachera à traiter plus précisément les problématiques suivantes:
– contribuer à l’amélioration des modèles de réseaux de neurones convolutifs (utilisation de GPU sur calculateurs régionaux/nationaux) avec des propositions méthodologiques et de nouvelles images récoltées au fil du stage;
– évaluer continuellement les performances des modèles sur les nouvelles images;
– confronter les alternatives de détection d’objet (plus coûteuses en temps) vis à vis de la simple classification;
– contribuer à l’élaboration d’une chaîne de traitement “du piège photo à l’identification puis la diffusion” qui permettra la reproductibilité des analyses et la mise à disposition des résultats, en suivant les principes de la Science Ouverte (principes FAIR)

Une sortie “terrain” en montagne pour l’installation ou la maintenance des pièges photographiques peut être envisagée si l’étudiant.e s’avère intéressé.e par cet aspect.

Profil du candidat :
L’étudiant/e devra présenter de fortes compétences en machine learning pour la vision par ordinateur, en programmation Python et maîtriser parfaitement les environnements Linux.

Un intérêt pour les questions de biodiversité serait un plus (le stage permet en effet de découvrir de nombreuses problématiques relatives à la conservation de la faune sauvage en France).

Formation et compétences requises :
Ecole d’ingénieur dernière année, Master 2 en informatique/mathématiques ou bien césure.

Adresse d’emploi :
Campus UCBLyon-Villeurbanne La Doua / campus USMB Chambéry-Technolac / campus UGA Grenoble-St Martin D’hères

Les laboratoire d’Ecologie Alpine (LECA, Chambéry-Grenoble) et Biométrie et Biologie Evolutive (LBBE, Lyon) regroupent des écologues, des biologistes et des méthodologistes. En particulier, ils forment l’épicentre rhône-alpin de l’écologie des communauté, discipline dédiée à la compréhension de l’organisation et du fonctionnement des écosystèmes. Ces laboratoires sont reconnus pour l’excellence de leurs développements méthodologiques pour l’écologie.

Temporal phenotyping of patients from EHR data based on tensor decomposition

Offre en lien avec l’Action/le Réseau : DOING/– — –

Laboratoire/Entreprise : Inria Lyon
Durée : 4 – 6 mois
Contact : thomas.guyet@inria.fr
Date limite de publication : 2022-02-01

Contexte :
**Supervising environment**

The project is proposed to contribute to the chair AI-RACLES funded by Inria-APHP-CS. Inria is the French national institute for digital science. APHP is the greater Paris university Hospital. And Central Supelec (CS) is a prestigious engineering school. AI-RACLES aims at developing artificial intelligence techniques to better exploit the APHP data lake to improve healthcare system and practices, especially for fragile patients.

The internship is proposed by two chair holders of AI-RACLES (Thomas Guyet and Pr. Etienne Audureau) and it will be supervised by:
* Thomas Guyet, Inria, Lyon thomas.guyet@inria.fr
* Pr. Etienne Audureau, APHP/UPEC, CEpiA (Clinical Epidemiology and Ageing), CHU Henri Mondor, etienne.audureau@aphp.fr
* Romain Tavenard, Univ. Rennes/LETG, romain.tavenard@univ-rennes2.fr

There will be opportunities for a funded PhD position after the internship.

**Context**

The APHP data lake is a huge Electronic Health Records (EHR) repository of the patients being admitted in one of the hospitals located in the greatest Paris. The database contains information about patient visits, including the care and drugs delivered along each of their visit (with their timestamps). For example, the APHP identified a cohort of more than 20,000 patients hospitalized during the Covid-19 crisis. A dataset was thus created from information on their condition and the care they received. This information constitutes their care pathway.

The main objective of the chair AI-RACLES is to develop new artificial intelligence techniques to analyze this data lake in order to address health questions. The context of this internship is to investigate how to support the evaluation of health care pathways. The notion of health care pathways denotes the sequence of cares of a patient being cured for a given disease. Quality assessment aims to identify the key characteristics of pathways which may likely leads to a positive outcome for the patient. For example, in the case of the Covid-19 crisis, it is interesting to identify the care strategies that would prevent patients from requiring intensive cares.

The first step to achieve this objective is to describe the actual care pathways. The APHP data lake gives us the opportunity to describe objectively the care pathways of patients from historical data. This internship aims to contribute to identifying the care pathways through the unsupervised or semi-supervised machine learning techniques.

Sujet :
The proposed research direction is the use of a powerful unsupervised machine learning technique called tensor factorization (or tensor decomposition).

In the context of EHR data analysis, tensor is seen as a three-dimensional tensor whose dimensions are the patient identifier, the time and the medical events (procedures, labtests, drugs delivered. The decomposition of two dimensional tensors allow the identification of typical patient profiles (the medical events per patients), which are called phenotypes. A care pathway is then represented by the sequence of the phenotypes.

The problem of tensor decomposition is an old statistical problem for which statistical approaches have been proposed since the early years of the past century. But in recent years, this problem is renewed on the light of machine learning, and neural networks. Several recent neural networks architecture have been proposed. They proved the feasibility of the approach to decompose efficiently large and complex tensors. In parallel, the interest of phenotyping from EHR data has also been highlighted in the biomedical literature.

In this internship, we would like to investigate the notion of temporal phenotypes, and temporal phenotyping. Contrary to a phenotype that gives a combination of medical events at one time instant, a temporal phenotype describes a temporal arrangement of medical events. It is thus more expressive and may be useful to identify short-term procedures that make the care pathways.

A similar objective is targeted by Emonet et al. with Temporal Analysis of Motif Mixtures (TAMM). The problem of identifying temporal phenotypes (topic models) is addressed by a non-parametric Bayesian model fitted using Gibbs sampling. One of the limitation of the proposal is the slowness and resources consumption of the solving technique, and a rigid model (modifying the model requires deriving a new sampler).

A starting point of the internship will be to adapt the model of TAMM to solve it using machine learning techniques and to evaluate it (from the efficiency and accuracy points of view). Then, the implemented model will be applied to extract temporal patient phenotypes from the APHP Covid-19 cohort data and contribute to 1) describing Covid-19 patients, possibly by criticality group, and 2) describing hospitalizations by conditions (comparison of new and historical ICUs). A secondary objective is to investigate the possibility of using these models to create discriminant temporal phenotypes, i.e. phenotypes that would occur more likely in a group of patients than in the others.

Profil du candidat :
* You are enthusiastic about research, you love to understand in depth the problems and to find them elegant solutions.
* You have an strong background in math and computer science (Python for machine learning environment).
* You are interested in artificial intelligence and, more precisely, in machine learning, optimization techniques, data analysis, …
* You have interest in the field of health and to contribute to the development of solutions that may help clinicians or epidemiologists.
* You speak and write English and/or French.

Formation et compétences requises :
* You are student in a Master 2 in computer science, data science or statistics, or student in a engineering school.

Adresse d’emploi :
* Location: Lyon (or possibly Paris). The intern will be hosted at Inria Lyon located on the Doua scientific campus, at Villeurbanne. Some meeting will be organized in Paris.
* Data access is secured
* application by mail with CV, motivation letter, transcripts
* Start date between february to may (4 to 6 months)

Document attaché : 202111151133_sujet_APHP.pdf

CFP – The 23rd IEEE Mobile Data Management – MDM 2022

Date : 2022-06-06 => 2022-06-09
Lieu : Paphos, Cyprus

****************************************************************************
C A L L F O R P A P E R S – R E S E A R C H T R A C K

IEEE MDM 2022 – The 23rd IEEE Intl. Conference on Mobile Data Management

Coral Beach Hotel & Resort, Paphos, Cyprus (hybrid format)

June 6 – 9, 2022

Home

Abstract Deadline: January 14, 2022!
https://cmt3.research.microsoft.com/MDM2022
****************************************************************************

The MDM series of conferences, since its debut in 1999, has established
itself as a prestigious forum for the exchange of innovative and
significant research results in mobile data management. The conference
provides unique opportunities to bring researchers, engineers, and
practitioners together to explore new ideas, techniques, and tools,
and exchange experiences.

Continuing its history, MDM 2022 seeks submissions of original research
contributions in the intersection of mobile computing and data management.

We welcome papers on topics including, but not limited to:
– Mobile Data Analytics
– Machine Learning/AI for Mobile Data
– Location and Trajectory Analytics
– Mobile Cloud Computing and Data Management in the Mobile Cloud
– Mobile Crowd-Sourcing and Crowd-Sensing
– Mobile Location-Based Social Networks
– Mobile Recommendation Systems
– Context-aware Computing for Intelligent Mobile Services
– Behavioral/Activity Sensing and Analytics
– Data Management for Internet of Things (IoT) and Sensor Systems
– Data Management for Augmented Reality Systems
– Data Management for Connected Cars, Intelligent Transportation
Systems, Smart Spaces
– Theoretical Foundations of Data-intensive Mobile Computing
– Data Stream Processing in Mobile/Sensor Network
– Indexing, Optimization and Query Processing for Moving Objects/Users
– Middleware and Tools for Mobile and Pervasive Computing
– Privacy and Security in Mobile Systems
– Routing, Personalized Routing, Eco-Routing, Routing for Electrical Vehicles
– Transportation-As-A-Service, Mobility-As-A-Service
– Innovative Applications driven by Mobile Data

Due to the COVID-19 global pandemic, MDM 2022 will be offered in a hybrid
format with details to be announced at a later stage. This decision will
alleviate the inherent difficulties and travel restrictions incurred
by the pandemic, offering the widest spread of new scientific knowledge
with the lowest risk to participants. The Organizers are committed in
offering the best possible physical and online experience capitalizing
and expanding on the success of earlier organizations.

* Submission Guidelines *
*************************
Please use the following URL link for submissions of Research Track Papers:

https://cmt3.research.microsoft.com/MDM2022

All submissions need to follow IEEE Computer Society Proceedings
Manuscript Formatting Guidelines. See templates here:
https://www.ieee.org/conferences/publishing/templates.html

The following are the page limits:
– Regular papers: 10 pages
– Short papers: 6 pages

Note that a paper exceeding the page limit in the respective category may
be rejected without review. If there are any appendices, they are counted
within the page limit.

Submission of a meaningful abstract by the abstract deadline is a
precondition for paper submission.

* Dates *
*********
Research Track

– Abstract Deadline January 14, 2022
– Submission Deadline January 21, 2022
– Notification of Acceptance March 4, 2022
– Camera-Ready & Author Registration Deadline April 22, 2022
– Early Registration Deadline May 13, 2022

* Publisher *
*************
IEEE CPS: https://www.computer.org/conferences/cps

All accepted papers will be published in the proceedings of the
2022 International Conference on Mobile Data Management and included
in the IEEE Xplore® digital library

* Award *
The conference will confer a Best Paper Award from the submission to
the Research Track of MDM 2022.

************************
* Organizing Committee *
************************

+ General Co-Chairs
Mohamed F. Mokbel (University of Minnesota, USA)
Jianliang Xu (Hong Kong Baptist University, Hong Kong)
Demetris Zeinalipour (University of Cyprus, Cyprus)

+ PC Co-Chairs
Mohamed Sarwat (Arizona State University, USA)
Xing Xie (Microsoft Research Asia, China)
Karine Zeitouni (University of Versailles Saint-Quentin, France)

+ Workshop Co-Chairs
Takahiro Hara (Osaka University, Japan)
Nikos Mamoulis (University of Ioannina, Greece)

+ Advanced Seminars Co-Chairs
Maria Luisa Damiani (University of Milan, Italy)
Sanjay Madria (Missouri University of Science and Technology, USA)
Manos Papagelis (York University, Canada)

+ Demo Co-Chairs
George Fakas (Uppsala University, Sweden)
‪Andreas Konstantinidis (Frederick University of Technology, Cyprus)
Matthias Renz (University of Kiel, Germany)

+ Industry Co-Chairs
Jie Bao (JD.com, China)
Christian Becker (University of Mannheim, Germany)
Lei Chen (Hong Kong University of Science and Technology, Hong Kong)

+ Keynote Co-Chairs
Wang-Chien Lee (Pennsylvania State University, USA)
Dimitrios Gunopulos (National and Kapodistrian University of Athens)
Xiaofang Zhou (Hong Kong University of Science and Technology, Hong Kong)

+ Panel Co-Chairs
Christophe Claramunt (Ecole Navale, France)
Baihua Zheng (Singapore Management University, Singapore)

+ Diversity & Inclusion Co-Chairs
Panos K. Chrysanthis (University of Pittsburgh, USA)
Vana Kalogeraki (Athens University of Economics, Greece)

+ PhD Colloquium Co-Chairs
Hua Lu (Roskilde University, Denmark)
Mohamed Sharaf (United Arab Emirates University, UAE)

+ Test-of-Time Award Committee
Karl Aberer (École polytechnique fédérale de Lausanne, Switzerland)
Christian S. Jensen (Aalborg University, Denmark
Kian-Lee Tan (National University of Singapore, Singapore)

+ Publicity Co-Chairs
Ahmed Eldawy (University of California – Riverside, USA)
Xiao Pan (Shijiazhuang Tiedao University, China)
Dimitris Sacharidis (Université Libre De Bruxelles, Belgium)

+ Sponsorship Chair
Konstantinos Pelechrinis (University of Pittsburgh, USA)

+ Proceedings Chair
Edison Chan (Hong Kong Baptist University, Hong Kong)

+ Online Platform Co-Chairs
Constantinos Costa (University of Pittsburgh, USA)
Paschalis Mpeis (University of Cyprus, Cyprus)

+ Steering Committee Liaison
Panos K. Chrysanthis (University of Pittsburgh, USA)

+ Finance Chair
George Pallis (University of Cyprus, Cyprus)

+ Website Management and Local Arrangements
Petros Stratis (Easyconferences, Cyprus)
Nicolas Kantzilaris (Easyconferences, Cyprus)

—
Karine Zeitouni, TPC Co-Chair
DAVID Lab., UVSQ, Université Paris Saclay
https://pages.david.uvsq.fr/kzeitouni

Lien direct

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Sécurisation des analyses en ligne d’entrepôts de données partagés – Cryptographie

Offre en lien avec l’Action/le Réseau : DOING/– — –

Laboratoire/Entreprise : ERIC Lyon
Durée : 5-6 mois
Contact : jerome.darmont@univ-lyon2.fr
Date limite de publication : 2022-02-02

Contexte :
Ce stage se déroulera dans le cadre de l’ANR BI4people ( https://eric.univ-lyon2.fr/bi4people/ ). L’utilisation des technologies de la Business Intelligence (BI) telles que les entrepôts de données et les techniques d’analyses en ligne (OLAP) restent complexes et réservées à des spécialistes. L’objet de cette ANR est de simplifier ces outils afin de les rendre accessible au plus grand nombre (petites entreprises, associations, etc.).

Sujet :
Dans ce contexte, il est important de permettre aux utilisateurs de pouvoir partager leurs données et leurs analyses. Ces aspects collaboratifs induisent des problèmes de confidentialité de données. Plus généralement, on peut considérer des scenarios où la confidentialité des données ou des requêtes doit être garantie. On pourrait également imaginer que des utilisateurs agissent de manière malveillante afin d’altérer les calculs et de compromettre le résultat des requêtes.

Quelques solutions sont proposées dans la littérature [1, 2]. Les plus abouties en termes de sécurité sont basées sur des primitives cryptographiques récentes, appelées FHE (Fully Homomorphic Encryption). Ces solutions n’ont à ce jour qu’un intérêt théorique, puisque les FHE existantes ne sont pas encore suffisamment performantes [3]. Pour obtenir des solutions utilisables en pratique, il est donc nécessaire de dégrader la sécurité ou le type de requêtes prises en charge. Des hypothèses sur les utilisateur·trices peuvent aussi être introduites, comme par exemple la proportion d’utilisateurs malveillants, le fait qu’ils soient coalisés ou non, etc.

L’objectif de ce stage est d’explorer, d’évaluer et de comparer les solutions existantes. Suite à cette analyse de l’état de l’art, il s’agira de proposer des solutions dédiées à la problématique et aux contraintes spécifiques du projet BI4 people.

Profil du candidat :
Bac + 5 en informatique

Formation et compétences requises :
Compétences avancées (niveau M2) en informatique. Notions de cryptographie ou de sécurité informatique fortement souhaitées.

Adresse d’emploi :
Laboratoire ERIC
Université Lyon 2
5 avenue Pierre Mendès France
69676 Bron Cedex

Document attaché : 202111121307_StageBI4people4.pdf

DataPlat 2022, the 1st International Workshop on Data Platform Design, Management, and Optimization

Date : 2022-03-29
Lieu : Edinburgh

Call For Papers for DataPlat 2022, the 1st International Workshop on Data Platform Design, Management, and Optimization, which will be held on March 29, 2022 at Edinburgh, co-located with EDBT/ICDT.

DataPlat focuses on the challenges originating from the paradigm change imposed by big data, which has triggered the evolution of information systems into complex data platforms or data ecosystems supporting data-intensive storage, computation, and analysis of data with heterogeneous structures. Over the last years, several research proposals have been made concerning vertical solutions that address different parts of the data management lifecycle within complex data-intensive ecosystems. DataPlat is aimed at funneling these efforts towards the development of data platforms as data-intensive ecosystems supporting data scientists and architects at a high level, and fosters innovative research solutions that contribute to further advancements in this field. DataPlat covers the topics of metadata modeling, collection, and storage to capture the distinguishing features of the data; the enabling of advanced functionalities spanning from research and data profiling to provenance control, orchestration of data transformation pipelines, incremental data integration, and efficient querying; data integration and querying within heterogeneous storage and computation engines, including multi-model DBMSs, polystores and cloud storage systems; the simplification of data management and fruition by data scientists, including artificial intelligence solutions and AutoML techniques.

The deadline for paper submission is December 12, 2021.

Authors of the best papers will be invited to submit an extended version to a Special Issue with Elsevier’s Future Generation Computer Systems (FGCS) journal (IF: 7.187).

For further information on the workshop, please head to https://big.csr.unibo.it/dataplat2022/

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