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« Données massives et/ou complexes en Santé Publique » Ingénieur hospitalier Remplacement congé maternité

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

Laboratoire/Entreprise : CHU Grenoble Alpes
Durée : 1 an
Contact : jlbosson@chu-grenoble.fr
Date limite de publication : 2022-07-20

Contexte :
Intégrer une équipe dynamique multidisciplinaire sur la valorisation des données de Santé au service des patients et du soins vous intéresse ?
Le CHU de Grenoble Alpes recrute pour un CDD d’un an à partir de Juillet 2022 (remplacement) un ingénieur pour intégrer l’équipe en charge de son Entrepôt de Données de Santé PREDIMED au sein du Pôle Santé Publique.
Profil Informatique médicale, technologie Santé big data, gestion de projet.
Connaissance du monde hospitalier souhaité. Profil junior accepté

Sujet :
Cette proposition de CDD vise à assurer la continuité du rôle de responsable du pôle projet de PREDIMED en se focalisant sur quelques missions essentielles au bon fonctionnement de l’ensemble du projet et de lien entre les différents pôles et acteurs de PREDIMED.
Il s’agit dans le détail de contribuer à des missions transversales indispensables (0,75 ETP) et d’être en charge de manière opérationnelle (requête, extraction, analyse, rapport) pour 0,25 ETP de la mise en place d’un nouveau projet (NOVARTIS) et du suivi d’un projet existant (DEMETER).

Profil du candidat :
Savoir et Savoir-Faire
– Connaissances du monde de la Santé, de son organisation, de ses contraintes éthiques et réglementaires en termes d’information de Santé
– Assurer le recueil des besoins des équipes cliniques et le dialogue avec les équipes PREDIMED
– Être source de propositions à des problématiques (techniques, médico-légales, autres) préalablement identifiées ;
– Concevoir, rédiger une documentation technique ;
– Concevoir, valider, mettre en oeuvre et tracer des requêtes appliquées à des bases de données ;
– S’exprimer face à différents publics, en français et en anglais ;
Savoir-être
– Dynamisme, Rigueur, Compétences relationnelles, Initiative, Autonomie, Travail en équipe, communication.

Formation et compétences requises :
Compétences techniques
– Avec formation initiale en mathématiques et informatiques, en système d’information, base de données,
– Niveau scientifique bac+5
– Connaissance des systèmes d’informations hospitaliers et des architectures big data
– Compétences en gestion de projet et communication

Adresse d’emploi :
CHU Grenoble Alpes, Grenoble, France

Document attaché : 202206031248_CHUGA «Données massives etou complexes en Santé Publique», Ingénieur hospitalier, remplacement congé maternité.pdf

Non-Stationary and robust Reinforcement Learning methodologies for surveillance applications

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

Laboratoire/Entreprise : Laboratoire des signaux et systèmes (L2S), Univers
Durée : 3 ans
Contact : stefano.fortunati@centralesupelec.fr
Date limite de publication : 2022-09-04

Contexte :
Reinforcement Learning (RL) methodologies are currently adopted in different context requiring sequential decision-making tasks under uncertainty [1]. The RL paradigm is based on the perception-action cycle, characterized by the presence of an agent that senses and explores the unknown environment, tracks the evolution of the system state and intelligently adapts its behaviour in order to fulfil a specific mission. This is accomplished through a sequence of actions aiming at optimizing a pre-assigned performance metric (reward). There are countless applications that can benefit from this perception-action cycle (traffic signal control, robots interactions the physical objects, just to cite a few), each of which is characterized by a peculiar definition of “uncertainty” or “unknown environment”. A more precise definition of this uncertainty strongly depends on the particular domain considered. However, there is at least one crucial assumption underlying the majority of classical RL algorithms: the stationarity of the environment, i.e. the statistical and physical characterization of the scenario, is assumed to be time-invariant. This is clearly a quite restrictive limitation in many real-world RL applications, where the agent is usually embedded in a changing scenario whose both statistical and physical characterization may evolve over time. Due to the crucial importance of including the non-stationarity in the RL framework, both theoretical and application-oriented non-stationary approaches have been proposed recently in the RL literature (e.g. [2], [3]). Among the numerous potential applications, in this project we will focus on the problem of Cognitive Radar (CR) detection in unknown and non-stationary environment. Specifically, building upon the previous works [4], [5], we will aim at proposing an RL based algorithm for cognitive multi-target detection in the presence of unknown, non-stationary disturbance statistics. The radar acts as an agent that continuously senses the unknown environment (i.e., targets and disturbance) and consequently optimizes transmitted waveforms in order to maximize the probability of detection (PD) by focusing the energy in specific range-angle cells.

Sujet :
The scientific goal of the proposed PhD thesis is twofold. Firstly, the PhD candidate will get familiar and develop original RL-based algorithms for non-stationary environments. These theoretical outcomes will be then applied to a specific scenario of great interest nowadays: the radar detection of drones. More specifically, the PhD thesis will be structured in two steps:
1. Theoretical foundation of non-stationary RL algorithms: The aim of this first step is to develop an original theoretical foundation of non-stationary Markov Decision Processes (MDP) [2]. In particular, the candidate will investigate the possibility to generalize classical RL methodologies to MDP characterized by a time-varying sets of states, actions and reward functions. This non-stationary generalization is of crucial importance for a wide variety of applications and it is an almost unexplored research field.
2. Surveillance applications and drone detection: The theoretical results obtained in the first part of the PhD thesis will then be used to derive and implement new algorithms for drones detection and tracking using radar systems [4], [5]. Specifically, we will consider a co-located Multiple-Input-Multiple-Output (MIMO) radar with a large (“massive”) number of transmitters and receivers. It has been shown, in fact, that this massive MIMO configuration allows one to dispense with unrealistic assumptions about the a-priori knowledge of the statistical model of the disturbance [4].

[1] R. S. Sutton, A. G. Barto (2018). Reinforcement Learning: An Introduction. MIT press, Cambridge,
[2] E. Lecarpentier, E. Rachelson, “Non-Stationary Markov Decision Processes, a Worst-Case Approach using Model-Based Reinforcement Learning,” Advances in Neural Information Processing Systems 32 (NeurIPS 2019), 2019, pp. 7214–7223.
[3] S. Padakandla, K. J. Prabuchandran, S. Bhatnagar, “Reinforcement learning algorithm for non-stationary environments,” Applied Intelligence 50, 3590–3606 (2020).
[4] S. Fortunati, L. Sanguinetti, F. Gini, M. S. Greco, and B. Himed, “Massive MIMO radar for target detection,” IEEE Transactions on Signal Processing, vol. 68, pp. 859–871, 2020.
[5] A. M. Ahmed, A. A. Ahmad, S. Fortunati, A. Sezgin, M. S. Greco, and F. Gini, “A reinforcement
learning based approach for multitarget detection in massive MIMO radar,” IEEE Transactions on Aerospace and Electronic Systems, vol. 57, no. 5, pp. 2622–2636, 2021.

Profil du candidat :
This interdisciplinary project requires skills in statistical signal processing and machine learning, with specifical focus on Reinforcement Learning. Basic knowledge of radar principles may be useful but not required. Concerning the programming languages, the candidate should have a good knowledge of Matlab and possibly of Python.

Formation et compétences requises :
1) Statistics,
2) Reinforcement Learning,
3) Statistical Signal processing.

Adresse d’emploi :
Laboratoire des signaux et systèmes (L2S),
bât. Bréguet, 3, rue Joliot Curie,
91190 Gif-sur-Yvette.

Document attaché : 202206030915_PhD_Proposal_Fortunati.pdf

POSTDOC POSITION – ORLEANS (BD+IA)

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

Laboratoire/Entreprise : LIFO (Laboratoire d’Informatique Fondamentale d’
Durée : 12 mois
Contact : mirian@univ-orleans.fr
Date limite de publication : 2022-07-10

Contexte :
THE REGIONAL PROJECT: aims to develop methods and tools to first extract information from textual data by structuring it in a database graph, and then to manipulate this knowledge graph in an intelligent way.

Sujet :
Data Science Queries: Language and Algorithms

The goal is the development of a first version of a query system on graph databases, whose (declarative) query language would encompass predictive analysis – a term that combines data management, machine learning and optimisation, and which reflects the growing demand for such tools for handling data science problems.

Profil du candidat :
The candidate should have a PhD degree in computer science.

Formation et compétences requises :
The work involves knowledge in the domain of databases and of machine learning. Skills on at least one of these areas are required. The candidate should also be motivated to invest in the complementary field.

A good English level is also required.

French is not mandatory for candidates with a very good level of English and willing to learn French for daily life in France.

Adresse d’emploi :
The research work is conducted at the Laboratoire d’Informatique Fondamentale d’Orléans (LIFO), in France. The postdoctoral fellow should be physically present (i.e., the Postdoctoral position is not achievable by remote work).

Document attaché : 202206012145_AnnoncePostDoc.txt

16th IFAC / IFIP Workshop on Enterprise Integration, Interoperability and Networking

Date : 2022-10-24 => 2022-10-25
Lieu : La Valetta, Malta

In conjunction with the 3rd International Conference on Innovative Intelligent Industrial Production and Logistics (IN4PL), the 16th IFAC/IFIP Workshop on Enterprise Integration, Interoperability and Networking will take place 24 – 26 October 2022 – in Valletta, Malta.

Additionally we will hold IFAC TC5.3 and IFIP WG 5.8 meetings during the event.

SCOPE

In the context of the Factory of the Future, enterprises have to become S^3 (Enterprises: Smart, Sensing and Sustainable) Enterprises. These system-of-systems must adapt to be sustainable not only along the environmental but also economic dimensions. The sensed information must support smart decisions. In this context, enterprise integration, interoperability and networking are major disciplines that study how enterprise system-of-systems collaborate, communicate, and coordinate in the most effective way. Enterprise Integration aims at improving synergy within the enterprise so that sustainability is achieved in a more productive and efficient way. Enterprise Interoperability and Networking aim at more adaptability within and across multiple collaborating enterprises.

TOPICS OF INTEREST

Topics of interest include, but are not limited to:

Interoperability in a Big-Data Society

Cyber Physical Systems Interoperability

Cognitive Cyber-Physical Systems

Interoperability in the Context of Internet of Things

Artificial Intelligence-enabled Data Management

Artificial Intelligence Models for Interoperability

Ontology and Knowledge Extraction from Data Sets

This workshop is supported by IFAC and IFIP groups:

International Federation of Automatic Control: Technical Committees TC5.3 Integration and Interoperability of Enterprise Systems & TC3.1 Computers for Control.

International Federation on Information Processing: Workgroup WG5.8 Enterprise Interoperability

IMPORTANT DATES

Paper Submission: July 31, 2022

Authors Notification: September 9, 2022

Camera Ready and Registration: September 19, 2022

Details are found here: https://in4pl.scitevents.org/EI2N.aspx

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Call for Papers CoopIS 2022: Deadline extension

Date : 2022-10-24 => 2022-10-06
Lieu : Bozen-Bolzano, Italy

—————————————————————————–
Call for Papers
CoopIS 2022: The 28th International Conference on Cooperative Information Systems
October 04-07, 2022
Bozen-Bolzano, Italy
http://www.coopisconference.org

Proceedings: Springer LNCS

—————————————————————————–

=========================================

Important Dates:

Abstract submission: June 6 2022
Full paper submission: June 15 2022
Full paper notification: July 20 2022
Camera ready due: July 27 2022
Author registration due: July 31 2022

=========================================

Papers need to be submitted to EasyChair.

https://easychair.org/my/conference?conf=coopis2022

# Aim and Scope

The International Conference on Cooperative Information Systems is an established international event for presenting and discussing scientific contributions about technical, economical, and societal aspects of distributed information systems at scale.

The guiding theme of this 28th conference is “Information Systems in a Digital World”, with a particular focus on the following areas:

– Topic 1: Data, Information, and Knowledge Engineering
– Topic 2: Machine-Learning and Knowledge Discovery
– Topic 3: Process Analytics and Technology
– Topic 4: Semantic Interoperability and Open Standards
– Topic 5: Security and Privacy
– Topic 6: Internet of Things and Digital Twins
– Topic 7: Architecture and Management of Information Systems
– Topic 8: Human Aspects and Social Interaction in Information Systems
– Topic 9: Services and Cloud in Information Systems

For a detailed description of these topics, please see the conference Web site at www.coopisconference.org

Submissions
===========
Authors are invited to submit original, unpublished research papers that are not under review for any other conference, workshop, or journal. Papers must be written in English. The contributions should address research questions that relate to one of the topics listed above.

We particularly encourage:

1. Contributions that introduce and evaluate technological innovations (e.g. new techniques, tools, methods or software).

2. Empirical studies (e.g. quantitative data on the effects of novel approaches in technical, social, or economical terms).

3. Systematic surveys of emerging technologies and competing paradigms.

The questions addressed should both be practically relevant and appealing to the general IS field. Full papers should include a systematic evaluation of the contribution and relate this contribution to related scientific work. Short papers may present work supported by preliminary evidence only.

Submitted papers will be peer-reviewed by at least 3 reviewers. Papers are evaluated in terms of originality, significance, technical soundness, and clarity.

Submissions for full papers must not exceed 18 pages in the final camera-ready paper style. Short papers can cover up to 8 pages. Submissions must be laid out according to the final camera-ready formatting instructions and must be submitted in PDF format.

Each accepted paper must have one of its authors registered to the conference before the camera-ready deadline. The conference organizers reserve the right of removing a paper from the proceedings if no author is officially registered by the camera-ready deadline. Moreover, only papers that have been presented by their authors during the conference will be published in the conference proceedings.

The final proceedings will be published by Springer Verlag in their Lecture Notes in Computer Science (LNCS). Author instructions can be found at: https://www.springer.com/gp/computer-science/lncs/conference-proceedings-guidelines

It is mandatory to submit manuscripts in electronic form (in PDF format).

General Chairs
==============
Hervé Panetto, Université de Lorraine, CNRS, CRAN, TELECOM Nancy, France.
Walid Gaaloul, Institut Polytechnique de Paris – Télécom SudParis, SAMOVAR, France.

Program Chairs
==============
Paolo Ceravolo, University of Milan, Italy
Hajo Reijers, Utrecht University, The Netherlands

Publicity Chairs
==============
Zhangbing Zhou, School of Information Engineering, China University of Geosciences, P.R. China
Jean M. Simão, Federal University of Technology – Parana, Brazil

Program Committee (Tentative)
=================
Marco, Aiello, University of Stuttgart, Germany
Mehwish, Alam, FIZ Karlsruhe – Leibniz Institute for Information Infrastructure, AIFB Institute, KIT, Germany
Joao Paulo, Almeida, Federal University of Espirito Santo, Brazil
Abel, Armas-Cervantes, The University of Melbourne, Australia
Nour, Assy, Télécom Sudparis, France
Ahmed, Awad, University of Tartu, Estonia
Banu, Aysolmaz, Eindhoven University of Technology, Netherlands
Eduard, Babkin, LAPRADESS Laboratory, State University, Russia
Sebastian, Bader, Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS, Germany
Sylvio, Barbon jr, University of Trieste, Italy
Ingmar, Baumgart, FZI Research Center for Information Technology, Germany
Khalid, Belhajjame, PSL, Université Paris-Dauphine, LAMSADE, France
Narjes, Bellamine, University of Manouba, ENSI, RIADI LR99ES26, France
Salima, Benbernou, Université Paris Descartes, France
Djamal, Benslimane, Lyon 1 University, France
Mario Luca, Bernardi, University of Sannio, Italy
Javier, Berrocal, University of Extremadura, Spain
Xavier, Blanc, Bordeaux University, France
Athman, Bouguettaya, The University of Sydney, Australia
Hayet, Brabra, Télécom Sudparis, Frannce
Uwe, Breitenbücher, University of Stuttgart, Germany
Cristina, Cabanillas, Vienna University of Economics and Business, Austria
Richard, Chbeir, Univ. Pau & Pays Adour, UPPA/E2S, LIUPPA Anglet, France
Carlo, Combi, Università degli Studi di Verona, Italy
Marco, Comuzzi, Ulsan National Institute of Science and Technology, South Korea
Silvia, Dallavalle de Pádua, University of São Paulo, Brazil
Massimiliano, de Leoni, University of Padua, Italy
Johannes, De Smedt, KU Leuven, Belgium
Bruno, Defude, Télécom Sudparis, France
Adela, del Río, University of Seville, Spain
Daniele, Dell’Aglio, University of Zurich, Switzerland
Elena, Demidova, L3S Research Center, Germany
Benoît, Depaire, Hasselt University, Belgium
Giuseppe, Desolda, Dipartimento di Informatica – University of Bari, Italy
Jochen, Deweerdt, University of Antwerp, Belgium
Claudio, Di Ciccio, Sapienza University of Rome, Italy
Chiara, Di Francescomarino, Fondazione Bruno Kessler, Italy
Chiara, Di Francescomarino, Fondazione Bruno Kessler (FBK), Italy
Khalil, Drira, LAAS-CNRS, France
Marlon, Dumas, University of Tartu, Estonia
Rik, Eshuis, Eindhoven University of Technology, Netherlands
Javier A., Espinosa-Oviedo, Delft University of Technology, Netherlands
Ernesto, Exposito, Université de Pau et des Pays de l’Adour, France
Dirk, Fahland, Eindhoven University of Technology, Netherlands
Marcelo, Fantinato, University of São Paulo, Brazil
George, Feuerlicht, University of Economics, Czech Republic
Avigdor, Gal, Technion, Israel
Luciano, García-Bañuelos, Tecnológico de Monterrey, Mexico
Chirine, Ghedira, Université Lyon1, France
Chirine, Ghedira Guegan, IAE – Lyon 3 University, France
María Teresa, Gómez, University of Seville, Spain
José, Gonzalez Enriquez, University of Seville, Spain
Mohamed, Graiet, ISIM Monastir, Tunisia
Paul, Grefen, Eindhoven University of Technology, Netherlands
Daniela, Grigori, Laboratoire LAMSADE, University Paris-Dauphine, France
Georg, Grossmann, University of South Australia, Australia
Antonella, Guzzo, Università della Calabria, Italy
Mohand-Said, Hacid, Université Claude Bernard Lyon 1 – UCBL, France
Mirian, Halfeld Ferrari Alves, University of Orléans, France
Armin, Haller, Australian National University, Australia
Karl, Hammar, Jönköping University, Sweden
Martin, Hepp, Universität der Bundeswehr München, Germany
Anett, Hoppe, TIB Leibniz Information Centre for Science and Technology, Poland
Stijn, Hoppenbrouwers, HAN University of Applied Sciences, Netherlands
Stefan, Jablonski, University of Bayreuth, Germany
Yaser, Jararweh, Duquesne University
Manfred, Jeusfeld, University of Skövde, School of Informatics (IIT), Germany
Andrés, Jiménez Ramírez, University of Seville, Spain
Anna, Kalenkova, University of Melbourne, Australia
Anna, Kalenkova, University of Melbourne, Australia
Dimka, Karastoyanova, University of Groningen, Netherlands
Dimitrios, Katsaros, Aristotle University of Thessaloniki, Greece
A. S. M., Kayes, La Trobe University
Matthias, Klusch, DFKI, Germany
Agnes, Koschmider, Kiel University, Germany
Marcello, La Rosa, The University of Melbourne, Australia
Agnieszka, Lawrynowicz, Poznan University of Technology, Poland
Alexander, Lazovik, University of Groningen, Netherlands
Maria, Leitner, Austrian Institute of Technology, Austria
Henrik, Leopold, Kühne Logistics University, Germany
Francesco, Leotta, “Dipartimento di Informatica e Sistemistica (DIS) “”A. Ruberti””, Univerità “”Sapienza”” Roma, Italy”
Mario, Lezoche, University of Lorraine, CNRS, CRAN, France
Xixi, Lu, Utrecht University, Netherlands
Jiangang, Ma, Federation University Australia
Zakaria, Maamar, Zayed University, UAE
Alexander, Mädche, Karlsruhe Institute of Technology, Germany
Sanjay, Madria, Missouri S & T, USA
Samira, Maghool, University of Milan, Italy
Maria, Maleshkova, University of Bonn, Germany
Amel, Mammar, Telecom SudParis, France
Felix, Mannhardt, Eindhoven University of Technology, Netherlands
Maristella, Matera, Politecnico di Milano, Italy
Raimundas, Matulevicius, University of Tartu, Estonia
Simon, Mayer, University of St. Gallen and ETH Zurich, Switzerland
Massimo, Mecella, Sapienza University of Rome, Italy
Lionel, Médini, LIRIS lab. / University of Lyon, France
Jan, Mendling, Humboldt University, Germany
Philippe, Merle, INRIA, France
Nizar, Messai, LI – Université François Rabelais Tours, France
Sellami, Mokhtar, Liris UCBL, France
Amira, Mouakher, Université de Bourgogne, France
Azzam, Mourad, Lebanese American University, Lebanon
Jorge, Munoz-Gama, Pontificia Universidad Católica de Chile, Chile
Juan Manuel, Murillo Rodríguez, University of Extremadura, Spain
Giulio, Napolitano, Fraunhofer Institute and University of Bonn, Germany
Alex, Ng, La Trobe University
Alexander, Norta, Tallinn University of Technology, Estonia
Selmin, Nurcan, Université Paris 1 Panthéon – Sorbonne, France
Andreas L, Opdahl, University of Bergen, Norway
Helen, Paik, University of New South Wales, Australia
Maria Luisa, Parody, University of Loyola, Spain
Oscar, Pastor Lopez, Universitat Politècnica de València, Spain
Cesare, Pautasso, University of Lugano, Switzerland
Stefan, Pickl, Uni Bw Munich, Germany
Geert, Poels, Ghent University, Belgium
Luise, Pufahl, TU Berlin, Germany
Gil, Regev, Ecole Polytechnique Fédérale de Lausanne, Switzerland
Manfred, Reichert, University of Ulm, Germany
Manuel, Resinas, University of Seville, Spain
Kate, Revoredo, Wirtschaftsuniversität Wien, Austria
Sonja, Ristic, University of Novi Sad, Serbia
Michael, Rosemann, Queensland University of Technology, Australia
Shazia, Sadiq, The University of Queensland, Australia
Flavia, Santoro, University of the State of Rio de Janeiro (UERJ), Brazil
Stefan, Schönig, Universität Regensburg, Germany
Gezim, Sejdiu, University of Bonn, Germany
Mohamed, Sellami, Telecom SudParis, France
Amartya, Sen, Oakland University, MI, USA
Estefanía, Serral, KU Leuven, Belgium
Nicolas, Seydoux, LAAS-CNRS/IRIT, France
Michael, Sheng, Macquarie University, Australia
Natalia, Sidorova, Eindhoven University of Technology, Netherlands
Jean M. Simão, Federal University of Technology – Parana, Brazil
Renuka, Sindhgatta, QUT, Australia
Pnina, Soffer, University of Haifa, Israel
Pnina, Soffer, University of Haifa, Israel
Jacopo, Soldani, University of Pisa, Italy
Chengzheng, Sun, Nanyang Technological University, Singapour
Yehia, Taher, DAVID – UVSQ, France
Joe, Tekli, Lebanese American University, Liban
Lucinéia Heloisa, Thom, Federal University of Rio Grande do Sul, Brazil
Farouk, Toumani, Limos, Blaise Pascal University, France
Nick, van Beest, Data61, Australia
Inge, van de Weerd, Utrecht University, Netherlands
Han, van der Aa, University of Mannheim, Germany
Jan Martijn, van der Werf, Utrecht University, Netherlands
Boudewijn, van Dongen, TU/e, Netherlands
Sebastiaan J., van Zelst, RWTH Aachen University, Germany
Maria Esther, Vidal, Universidad Simon Bolivar, Dept. Computer Science, Bolivia
Ingo, Weber, TU Berlin, Germany
George, Weichhart, Profactor AG, Austria
Tobias, Weller, Karlsruhe Institute of Technology, Germay
Lena, Wiese, Fraunhofer Institute for Toxicology and Experimental Medicine (ITEM), Germany
Karolin, Winter, Technical University of Munich, Germany
Guido, Wirtz, University of Bamberg, Germany
Moe Thandar, Wynn, Queensland University of Technology, Australia
Jian, Yang, Macquarie University, Australia
Jian, Yu, Auckland University of Technology, New Zeland
Amrapali, Zaveri, Maastricht University, Netherlands
Zhangbing, Zhou, CUG Beijing, P.R. China

Topic Descriptions

==================

Relevant topics for the conference include, but are not limited to the following:

Topic 1: Data, Information, and Knowledge Engineering
– Conceptual and Enterprise Modeling
– Ontology Learning and Engineering
– Data Structures in Cloud and Big Data Architectures
– Ontology and Schema Alignment and Interoperability
– Evolution and Maintenance of Conceptual Models
– Data Quality Management

Topic 2: Machine-Learning and Knowledge Discovery
– Machine Learning
– Deep Learning
– Data Mining
– Graph Embeddings
– Entity Recognition and Linking
– Linguistic Annotation

Topic 3: Process Analytics and Technology
– Performance Measurment
– Trace Encoding
– Process Discovery
– Conformance Analysis
– Process Improvement
– IT for work innovation
– Robotic Process Automation

Topic 4: Semantic Interoperability and Open Standards
– Vocabularies for the Semantic Web
– Formalisms and Syntaxes
– Querying Graph Data and Query Languages
– Inference and Reasoning
– Open Data Architectures and Ecosystems

Topic 5: Security and Privacy
– Access Control
– Data Proteciton and Privacy
– Blockchain-based Approaches
– Cryptocurrencies
– Information Entropy

Topic 6: Internet of Things and Digital Twins
– Connectivity, Interfaces, and Protocols
– Data Structures for IoT and Digital Twins
– Storage and Data Management
– Computing and Processing in IoT Environments
– Sensors and Actuators

Topic 7: Architecture and Management of Information Systems
– Information Architecture
– Enterprise Architecture Management
– Software Design
– Digital Transformation
– Monitoring Tools And Techniques

Topic 8: Human Aspects and Social Interaction in Information Systems
– Economics of Cooperative Information Systems
– Visualization
– Human-Computer Interaction
– Incentives and User Contributions
– Reputation Management
Topic 9: Services and Cloud in Information Systems
– Web Services, APIs
– Services Science, Engineering, Management
– Microservice-oriented architecture (MOA), Service Mesh
– Cloud service management, Cloud workflow management
– Cloud and fog computing, Edge service orchestration

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Call For Papers: GRAPH-QUALITY Workshop Co-located with ECML-PKDD, 23 September 2022

Date : 2022-09-23
Lieu : Grenoble

https://graphquality.github.io/

*************************************************************

[Please accept our apologies if you receive multiple copies of this (CFP)]

The GRAPH-QUALITY Workshop at ECML-PKDD 2022 aims to explore the theoretical and practical aspects of quality of data, models and evaluation in the context of graph-based data mining and machine learning. We invite contributions in the area of Data and Model Quality for Mining and Learning with relational data (measures, algorithms, models, tools, evaluations, etc.) to be presented at the GRAPH-QUALITY workshop which is to be held at the European Conference on Machine Learning and Principles and Practices of Knowledge Discovery from Data (ECML-PKDD), in Grenoble, France – 23 September 2022.

Topics of interests

Anomaly detection on graphs
Assessment of fairness and bias in the context of graphs, graph models (including representations), and subsequent tasks such as link prediction
Explainable graph models and predictions
Graph models and representations learning in the context of missing data and noise
Privacy preserving data mining and machine learning for relational data
Probabilistic methods and uncertainty estimation on networks
Algorithms and metrics for quality preservation on relational data

Submission Information

All papers will be peer-reviewed single-blind. We welcome many kinds of papers, such as (and not limited to):

Novel research papers
Work-in-progress papers
Vision and position papers
Appraisal papers of existing methods and tools (e.g., evaluations, lessons learned)

Authors should indicate in their abstract the kind of submissions that the paper belongs to, to help reviewers better understand their contributions. Submissions must be in PDF, written in English and formatted according to the single-column CEUR-ART style (downloadable at http://ceur-ws.org/Vol-XXX/CEURART.zip). Novel research papers should be 10 to 16 pages (including references), work-in-progress, vision/position, and appraisal papers should be 6-10 pages (including references). Accepted papers will be presented as a poster in the poster sessions and a few will be selected to also give an oral presentation.

Authors may opt-in to have their paper possibly published in workshop proceedings at CEUR-WS. All papers will be posted on the workshop website. The proceedings containing the opt-in papers will be submitted for inclusion to CEUR-WS. Assuming a sufficient number of high-quality papers, the proceedings are likely to be accepted, but conforming to CEUR-WS policy it cannot guarantee beforehand that the proceedings will indeed be published. Papers in the CEUR-WS series are published Open Access, without fee, under the CC-BY 4.0 licence (exceptions for Crown or US government employees). You as the author remain to hold the copyright.

For accepted papers, at least one author must register for the conference and attend the workshop in-person to present the work.

Submit via EasyChair: https://easychair.org/my/conference?conf=graphquality2022

Important Dates

Submission: June 20, 2022

Notification: July 18, 2022

Early registration deadline for the conference: July 22, 2022

Camera-ready: August 12, 2022

Programme and papers online: Monday 5 Sep 2022

Workshop date: September 23, 2022

Futher information and Contact

Organizers: Nidhi Hegde, Christine Largeron, Jefrey Lijffijt, Osmar R. Zaïane

Website URL: https://graphquality.github.io/

E-mail: graphquality22@gmail.com

Lien direct

Notre site web : www.madics.fr
Suivez-nous sur Tweeter : @GDR_MADICS
Pour vous désabonner de la liste, suivre ce lien.

Post doc in Information Retrieval

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

Laboratoire/Entreprise : IRIT, Toulouse
Durée : 1 year
Contact : josiane.mothe@irit.fr
Date limite de publication : 2022-08-31

Contexte :
This post doc is related to my work on machine learning for information retrieval, query difficulty and system adaption. We have been working for more than 10 years on this domain, have a huge amount of pre-treated data including features, methods, measures, etc…

Sujet :
Query performance prediction aims at predicting the level of effectiveness a system will have for a given query. There are many predictors that have been defined in the litterature, however the domain is still searching for a solution to make accurate predictions. Not only we need to predict the level of difficulty but also to adress the problem of difficult queries. Machine learning will be used. We have already a huge amount of data that we produced to be used in this project.

Profil du candidat :
Defended PhD on Information retrieval or machine learning, or Post doc on a domain related to the topic of the post-doc
English level: at least B2

Formation et compétences requises :
Computer scientist, with a good knowledge of maths
Top ranked during master studies, preferably in CS and/or applied mathematics
Good level of English
A good record of publications

The CV will include the awards, list of publications with their type and ranks, the marks or/and ranks during the Master. The names and emails of the supervisors (who could be contacted for short listed applicants)

Adresse d’emploi :
Toulouse, South France

Artificial Intelligence for Agrigulture

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

Laboratoire/Entreprise : Univ. de Brashov (Roumanie) / Univ. de Toulouse (I
Durée : 36
Contact : josiane.mothe@irit.fr
Date limite de publication : 2022-08-31

Contexte :
IA4AGRI is a European project where the Universities of Toulouse, Rome and Brashov work together.

Sujet :
The overall context is Artificial intelligence for agriculture; it includes working with Earth observation data, factual and textual data.

Profil du candidat :
You are interested on these topics?
You have a Master on related topics (Artificial intelligence, computer science, Earth observation, …)
Please send an email to Josiane.Mothe@irit.fr, along with your CV (inclusing your rank and awards), the topic you would like to work on, an any other information you find relevant (e.g. motivation).
Applications to be sent to Josiane.Mothe@irit.fr

Formation et compétences requises :
– Top ranked in your master degree
– At least B2 English
– Modules on Machine learning, Artificial intelligence, Image or data analysis

Adresse d’emploi :
You would like working in collaboration with Romania and Italy or France?

Cybersecurity for industrial networks

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

Laboratoire/Entreprise : ICube / Technology & Strategy
Durée : 36 mois
Contact : fabrice.theoleyre@cnrs.fr
Date limite de publication : 2022-06-30

Contexte :
Industry 4.0 is the novel industrial revolution, where objects are connected to a global network infrastructure. Fieldbus (e.g., CAN, modbus) interconnect the different devices to controllers. These objects are constrained in memory and computational capacity and may endanger the network infrastructure if they are corrupted. They may even jeopardize the safety of industrial applications.

Sujet :
We need to deploy Intrusion Detection Systems able to detect attacks and to patch (a.k.a. reconfigure) the network to contain the attacker. Fortunately, industrial applications rely on pre-existing specific properties that may help us to detect abnormal behaviors. The PhD student will exploit real datasets, and a prototyping testbed @ Technology & Strategy.

The PhD student will rely on Artificial Intelligence algorithms to implement an efficient Intrusion Detection System (Network IDS) able to detect anomalies, that deviate from a normal behavior.

Profil du candidat :
Master in Computer Science (major in AI, data science)

Formation et compétences requises :
Applicants should have solid skills in:
– Excellent programming skills, particularly in embedded systems (C);
– Excellent knowledge of Machine Learning techniques (not only as a user);
– Excellent data science language skills (R, or Python);
– Excellent communication and writing skills. Note that knowledge of French is not
required for this position;

Knowledge of the following technologies is not mandatory but will be considered as a plus:
– Networking protocols and stacks;
– Fieldbus communications;
– Revision control systems.

Adresse d’emploi :
Université de Strasbourg / Technology & Strategy
Both located in Strasbourg

Applications should be submitted by email to tands-cifre@icube.unistra.fr.

They must include:
– A Curriculum Vitae;
– List of 2 or 3 references to contact (position, email address);
– Transcripts of undergraduate and graduate studies;
– Link to MSc thesis, and publications if applicable;
– Link to personal software repositories (e.g. GitHub)

Please prefix the filenames of your application with your lastname.

Comparaison et coopération d’approches en analyse de concepts formels pour les données relationnelles

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

Laboratoire/Entreprise : ICube Strasbourg et IRISA Rennes
Durée : 36 mois
Contact : florence.leber@engees.unistra.fr
Date limite de publication : 2022-07-10

Contexte :
Dans les données disponibles pour l’analyse, beaucoup ont un caractère relationnel : données spatiales, temporelles, ou décrivant des liens entre individus. Les méthodes traditionnelles ne sont pas adaptées à ce type de données, qui nécessitent des approches spécifiques, incluant des techniques d’agrégation. Parmi ces approches, l’analyse relationnelle de concepts et l’analyse conceptuelle de graphes sont dérivées de l’analyse de concepts formels (ACF) [1], qui est une méthode mathématique de classification, largement appliquée sur différents types de données et dans de nombreux domaines (par exemple [2,3]). Elle consiste, à partir d’une table (appelée contexte) décrivant des objets par des attributs, à construire un treillis de concepts, i.e. des couples (extension ; intension) d’ensembles fermés décrivant les objets et les attributs qui les définissent.
L’analyse relationnelle de concepts (ARC) [4] considère deux types de contextes, des contextes objets-attributs et des contextes objets-objets décrivant les relations entre objets. L’ARC étend les contextes objets-attributs par des attributs relationnels de la forme qrC, où q est un quantificateur, r une relation et C un concept issu du co-domaine de r. Le résultat de l’ARC est une famille de treillis (un par contexte objets-attributs) reliés entre eux par ces attributs relationnels : un concept d’un treillis représente un groupe d’objets caractérisé par des attributs simples et des attributs relationnels renvoyant à des concepts d’un autre treillis.
L’analyse conceptuelle de graphes (Graph-FCA) [5] a pour contextes des hypergraphes où les nœuds sont les objets et où les hyperarcs sont étiquetés par des attributs. Un hyper-arc unaire a(o) correspond à la description d’un objet par un attribut, comme dans l’ACF. Un hyper-arc binaire a(o1,o2) correspond à une relation ‘a’ de o1 vers o2, comme les attributs relationnels dans RCA. Les relations n-aires sont représentées par des hyperarcs n-aires a(o1,…,oN). Un concept de graphe représente un ensemble de tuples d’objets (extension) qui peuvent être vus comme les réponses exhaustives à une requête conjonctive (intension), par exemple (x,y) ← a1(x,z), a2(y,z), et où cette requête exprime tout ce que ces tuples ont en commun.

Sujet :
Cette thèse s’inscrit dans le cadre de l’ANR SmartFCA, qui regroupe 5 équipes françaises travaillant dans le domaine de l’ACF et dont l’objectif est de mettre à disposition une plateforme rassemblant les différentes variantes de cette méthode. Plusieurs ingénieurs seront affectés au développement de cette plateforme.

Objectifs de la thèse : Cette thèse a pour but de mener une comparaison théorique et expérimentale des deux approches ARC et Graph-FCA, de proposer des éléments pour faire coopérer les deux approches, et de définir un guide méthodologique d’usage (modélisation des données, valeurs des paramètres, choix des algorithmes, etc.). Les résultats, algorithmes et guide méthodologique, seront intégrés dans la plateforme développée dans le cadre du projet ANR SmartFCA.
Les liens entre les deux approches ont déjà été abordés [6,7,8] et la thèse doit approfondir ces travaux. II s’agira dans un premier temps d’étudier et de comparer les deux approches, à partir des outils existants, en les testant sur des jeux de données relationnels fournis par les partenaires du projet. On s’intéressera en particulier à proposer un modèle déclaratif de l’ARC qui est actuellement définie de manière itérative. On s’intéressera aussi à la coopération entre l’ARC et Graph-FCA par la définition des structures de données permettant de les rendre interopérables.
Le caractère explosif des approches fondées sur l’ACF conduit à utiliser des algorithmes ne calculant qu’une sous-partie des concepts ou des treillis : AOC-poset [9], approches exploratoires, calcul de voisinages, estimation des résultats à partir du choix des paramètres [10,11] … Ces variantes seront aussi étudiées et permettront de définir un cadre méthodologique d’utilisation de l’ARC et de Graph-FCA incluant ces différentes options ainsi que des éléments pour guider leur usage. Le travail sera mené en coopération avec un ingénieur chargé des développements dans la plateforme.

Apports attendus :
• Avancées théoriques sur les méthodes ACF
• Développements méthodologiques
• Expérimentations et validation sur des données réelles

Profil du candidat :
Informatique, science des données, formalisation
• Curiosité, capacité à appréhender différents domaines et à interagir avec les experts de ces domaines

Formation et compétences requises :

• Master 2 en Informatique ou équivalent
• Formation en logique, représentation de connaissances et programmation

Adresse d’emploi :
ICube UMR 7357 – Laboratoire des sciences de l’ingénieur, de l’informatique et de l’imagerie
300 bd Sébastien Brant – CS 10413 – F-67412 Illkirch Cedex –

Document attaché : 202205301313_these_RCA_GraphFCA.pdf