4-1 | Master Informatique en Apprentissage et Traitement Automatique de la Langue : ATAL. Universités du Maine et de Nantes France
Les Universités du Maine et de Nantes propose un parcours conjoint de Master Informatique en Apprentissage et Traitement Automatique de la Langue : ATAL !
Le parcours ATAL forme des étudiants issus de filières informatiques à un ensemble de techniques d'apprentissage automatique et de traitement automatique de la langue qui sont au c?ur des applications en ingénierie des langues telles que la traduction automatique, la fouille d?opinions, la recherche d?information, la reconnaissance de la parole et du locuteur? Il s'agit donc de former des étudiants hautement spécialisés qui seront capables de mettre en ?uvre des applications prenant en compte des masses de données complexes et hétérogènes. Au terme de la formation les étudiants seront reconnus comme DataScientist, Chef de projet en ressources linguistiques, Cadre en technologies et services de l?information?
La formation s?appuie sur des chercheurs issus des laboratoires du LS2N (Laboratoire des Sciences du Numérique de Nantes) et du LIUM (Laboratoire d'Informatique de l'Université du Maine) et sur des acteurs économiques dont les applications nécessitent des connaissances sur le traitement de données langagières. En outre, la formation est très ancrée dans son écosystème régional et les étudiants seront invités à participer à des Meetup et sensibilisés au monde de l?entrepreneuriat.
Il est possible d?accéder à la formation en M1 comme en M2 selon les acquis du candidat.
- le M1 peut être indifféremment réalisé au Mans ou à Nantes selon la préférence de l?étudiant.
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4-2 | Virtual Coaches
Modeling Virtual Coaches
The European Horizon-2020 project Council of Coaches aims to develop a tool in which virtual embodied coaches form a team of experts that can discuss amongst themselves and with the user how the user could improve his healthy lifestyle behaviors. The project combines state of the art 3D Virtual Avatars with language and reasoning technology and applies this to the area of lifestyle and behavior change coaching.
The goal of research is to design, implement and evaluate user interaction with the team of virtual agents, the council of coaches (Home Interface) and one-on-one interaction with a single coach (Mobile Interface). In particular it focuses on
- Modelling coaches with their own specific agenda and behaviour style aiming at creating unique social relationships with users.
- Adapting virtual coaches? behavior to ensure that user maintains his involvement in the relationship with the coaches.
Specific attention will be given to the development of virtual coaches with a wide variety of behavioural characteristics and personality traits. Several studies have shown how low level features such as behaviour expressivity and high level characteristic (e.g. personality traits ) affect user?s involvement in their interaction with the virtual characters and impact his performances (McRorie et al., 2011) (Paiva et al., 2017)).
The work in this research will make use of and build upon the GRETA/VIB platform developed at UPMC (Pecune et al., 2014) for multimodal behaviour generation and for visualizing virtual coaches.
To apply, send a CV and names of reference to catherine.pelachaud@upmc.fr
H2020 Council of Coaches
Context:
The GRETA/VIB platform is developed at CNRS-ISIR (Pécune et al, 2014). It simulates virtual characters able to communicate with humans in real-time. It is endowed with socio-emotional capabilities. The control of the character is done through two specific languages at the communicative intention level and at the multimodal behavior one. The platform includes several tools to create multimodal behaviors.
The GRETA/VIB platform is used within the European Horizon-2020 project Council of Coaches which aims to develop a tool in which virtual embodied coaches form a team of experts that can discuss amongst themselves and with the user how the user could improve his healthy lifestyle behaviors. The project combines state of the art 3D Virtual characters with language and reasoning technology and applies this to the area of lifestyle and behavior change coaching.
Job Description:
We are looking for an engineer knowledgeable in 3D virtual environment. S/he will participate to Council of Coaches project. Her/his role will be:
- Port the Greta/VIB platform onto Android mobile and VR headset.
- Participate to development of computational model of agent?s multimodal behaviors
- Integrate software modules within the VIB/Greta platform and/or within a bigger software system developed within the H2020 project Coach Council.
- Participate to developers meetings (online and on site) of the H2020 project Coach Council
Profile: Engineer in computer science, Master, PhD
skills:
- Programming languages and standards: Java, C#, C++, XML, Ogre 3D
Mastered skills:
- The ideal candidate will have solid software development, testing and integration skills
- English (written and oral)
Project Length: 1 year position renewable
Place: ISIR - UPMC
Stipend: depends on applicant qualification
Contact: Catherine Pelachaud, CNRS-ISIR; catherine.pelachaud@upmc.fr
To apply, send a CV, names of reference, master grades (for Master applicants) to catherine.pelachaud@upmc.fr
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4-3 | IEEE CIS Newsletter on Cognitive and Developmental Systems (open access).
Dear colleagues,we are happy to announce the release of the latest issue of the IEEE CIS Newsletter on Cognitive and Developmental Systems (open access).This is a biannual newsletter addressing the sciences of developmental and cognitive processes in natural and artificial organisms, from humans to robots, at the crossroads of cognitive science, developmental psychology, artificial intelligence, machine learning and neuroscience. It is available at: http://goo.gl/pA7WrHFeaturing dialog:=== 'One developmental architecture to rule them all?'
== Dialog initiated by Matthias Rolf, Lorijn Zaadnoordijk and Johan Kwisthout
with responses from: Niels Taatgen, John Spencer, Gary Jones, Gerard Wolff, Clément Moulin-Frier and Paul Verschure == Topic: Humans have a unique capability to achieve and learn a wide diversity of skills of all kinds, from low-level sensorimotor skills to very abstract linguistic or mathematical skills. Is it possible to develop theories of how general cognitive architectures can display such a general flexibility for skill learning? This dialog adresses this question, and discusses whether and how it would be useful both epistemologically and in practice to aim towards the development of a ?standard integrated cognitive architecture?, akin to ?standard models? in physics, or whether focusing on simple and partial models should be a better approach. In particular, this question is discussed in the context of understanding development in infants, and of building developmental architectures, thus addressing the issue of architectures that not only learn, but that are adaptive themselves.
Call for new dialog:=== 'Curiosity as Driver of Extreme Specialization in Humans'== Dialog initiated by Celeste Kidd== This dialog asks the question of why and how humans can be driven to extremely specialize. In particular, it proposes the hypothesis that curiosity may play a fundamental role in this process, and highlights many important open questions about how this could happen, and what are the actual mechanisms of curiosity-driven exploration and learning. Those of you interested in reacting to this dialog initiation are welcome to submit a response by May 30th, 2018. The length of each response must be between 600 and 800 words including references (contact pierre-yves.oudeyer@inria.fr).
Let us remind you that all issues of the newsletter are all open-access and available at: https://goo.gl/ZjjZNzI wish you a stimulating reading!Best regards,Pierre-Yves Oudeyer,Editor of the IEEE CIS Newsletter on Cognitive and Developmental SystemsResearch director, InriaHead of Flowers project-teamInria and Ensta ParisTech, Francehttp://www.pyoudeyer.com
and
Fabien Benureau
Assistant Editor
Inria Mnemosyne team
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4-4 | new Masters program in Natural Language Processing and Data Science - Computer Science, Speech, Language, and Knowledge Representation at University of Lorraine, Nancy, France
The Institute of Digital Sciences, Management and Cognition at the University of Lorraine (Nancy, France) opens a new Masters program in Natural Language Processing and Data Science - Computer Science, Speech, Language, and Knowledge Representation
http://institut-sciences-digitales.fr/idmc-master-degree-in-natural-language-processing/
Do you want to become an expert in Deep Neural Networks, Logic, Speech Processing, Information Retrieval, Knowledge Representation, ? all of this for Natural Language? Here's your chance!
Natural Language Processing (NLP) is at the crossroads of linguistics, computer science, and artificial intelligence. We propose a modern curriculum which combines different approaches covering both theoretical and applied perspectives.
The program includes a hands-on project in each semester. It ends with a 6-month paid internship in a company or a research lab.
See the course description below.
---------------------------------------------------------------------------------------- You can apply for the 1st year (M1) or for directly entering the 2nd year (M2). ----------------------------------------------------------------------------------------
Language ------------- All courses are taught in English (except the 'French for non-native speakers' course).
Fees ------- The University of Lorraine is publicly funded and therefore offers tuition-free education for all students including students from both inside and outside EU/EEA/EFTA countries. The only student expenditures are a nominal semester fee of about 600 euros which includes health insurance. Nancy?s high quality of life goes hand-in-hand with a low cost of living.
Why Nancy? --------------- Nancy is a center of excellence for NLP!
1/Two important research centers: - LORIA, the computer science research laboratory (http://www.loria.fr/en/) - ATILF the linguistics research laboratory (http://www.atilf.fr/)
Both research units participate in many national and international research projects. Their members are highly visible in the international community and regularly publish in the most prestigious conferences and journals in the field.
2/ The University of Lorraine participates in two Erasmus Mundus Masters funded by the European Union : - Erasmus Mundus Master Language and Communication Technology (http://lct-master.org/) - Erasmus Mundus Master EMLex (https://www.emlex.phil.fau.eu/)
Why NLP? -------------- Natural Language Processing is a rapidly expanding field.
With the proliferation of digital data, there is a massive need for well-trained engineers and researchers able to exploit this data for commercial (e-commerce, recommendation systems, translation, etc) and socio-political (e-learning, opinion mining, behavioral prediction, etc.) purposes.
Start-ups, small firms with strong R&D profiles and large companies (e.g., in France, Airbus, Thales, EDF, Orange) are looking for scientists and engineers who can contribute to the development of systems capable of managing and exploiting textual data.
The GAFAM (Google, Amazon, Facebook, Apple, Microsoft) are hiring intensively in both the NLP and the Speech processing domains.
The need for PhD candidates is also strong as shown by the many PhD scholarships offered by both academic and industrial players.
Contacts ------------ Head of the MSc program: Maxime Amblard <maxime.amblard@univ-lorraine.fr> Secretary: Karine Weisse <karine.weisse@univ-lorraine.fr> Location of the courses : École des Mines (Artem Campus, Tram stop 'Blandan') http://www.alliance-artem.fr/
Application --------------- The application file is available here: https://ecandidat.univ-lorraine.fr
More information: http://institut-sciences-digitales.fr/application-process-nlp-master-degree/#application
Academic Year 2018-2019 ----------------------------------- From September 3rd to 13th, 2018: Refresher courses in mathematics (probability theory, statistics) and computer science (algorithms, programming)
M1: 1st semester from September 17th to January 11th 2nd semester from January 14th to June 6th M2: 1st semester from September 17th to February 15th Internship from February to August
In brief ---------- ? A program with strong national and international visibility ? Taught entirely in English ? A high quality research environment with two research centers covering a wide range of disciplines (linguistics, computer science, speech and language processing) and applications (Human-Machine Interfaces, Speech Processing, Knowledge Retrieval, Statistical Machine Translation, etc.) ? Studies in a vibrant, human-sized city ? Studies at a low cost (Tuition fees at around 600 euros per year incl. health insurance)
Course Description --------------------------- M1 1st Semester --------------------------- PROBABILITIES, STATISTICS AND ALGORITHMS FOR AI ? Elementary mathematical tools, statistics, algorithms to define and solve artificial intelligence problems ? Case studies covering both theoretical and practical aspects
CORPUS DESIGN AND ACQUISTION ? Written corpora ? Speech corpora
SOFTWARE ENGINEERING ? Collection, analysis and formalization of customers' needs ? Software design and development ? Functional analysis and specifications ? Project management
LINGUISTICS FOR NLP (1) ? Methods for Natural Language Processing ? Phonology ? Morphology
PROJECT AND FOREIGN LANGUAGE ? Hands-on project ? Foreign language (French)
--------------------------- M1 2nd Semester --------------------------- MACHINE LEARNING AND SEMANTIC WEB ? Machine learning theory ? Data mining ? Knowledge retrieval
FORMAL TOOLS ? Logic ? Formal languages ? Calculability and complexity
DATA PROCESSING ? Storage and retrieval ? Data analysis
LINGUISTICS FOR NLP (2) ? Lexicology: lexical units and phraseology ? Syntax ? Semantics
PROJECT AND FOREIGN LANGUAGE ? Hands-on project ? Scientific communication ? Foreign language (French)
--------------------------- M2 1st Semester --------------------------- DEEP LEARNING AND DATA MINING ? Neural networks, Deep neural networks ? Data mining (structured data and text) ? Collaborative filtering
TEXT AND SPEECH PROCESSING ? Processing textual data ? Speech processing ? Terminology and ontology
NATURAL LANGUAGE PROCESSING AND DISCOURSE ? Application to text ? Computational semantics ? Discourse and Dialog modeling
LEXICONS AND GRAMMARS FOR NLP ? Diachronic and synchronic lexicology ? Lexical resources ? Syntactic framework
PROJECTS AND FOREIGN LANGUAGE ? Software project ? Law and ethics ? Research methods ? Professional integration ? Foreign language (French)
--------------------------- M2 2nd Semester --------------------------- Paid internship (company or research lab)
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4-5 | M.Sc. Program in Natural Language Processing (NLP) and Data Science, Université de Lorraine, Nancy (France)
M.Sc. Program in Natural Language Processing (NLP) and Data Science Université de Lorraine, Nancy (France)
The Institute of Digital Sciences, Management and Cognition is opening a Masters Program in NLP - ?? Computer Science, Speech, Language and Knowledge Representation
**************
http://institut-sciences-digitales.fr/idmc-master-degree-in-natural-language-processing/ **************
So you want to be a specialist in Neural Networks, Logic, Speech Processing, Information Retrieval, Knowledge Representation... all for Natural Language? Well, now's your chance!
Natural Language Processing (NLP) lies at the crossroads of linguistics, computer science and artificial intelligence. This Masters Program offers a modern curriculum which combines the different approaches, and covers both theoretical and applied perspectives.
In each semester, the program includes a hands-on project. It ends with a 6-month paid internship in a company or a research lab. You can find the course description below.
---------------------------------------------------------------------------------------- You can apply to directly enter at either the first year (M1) or second year (M2) level. ----------------------------------------------------------------------------------------
Language ------------- All courses are taught in English (except the 'French for non-native speakers' class).
Fees ------- The University of Lorraine is publicly funded and thus offers tuition-free education for many students including students from the EU (243 ? cost per year). The tuition fee for students from other nations arriving in France at the beginning of the 2019 academic year will be 3.770 ? per year.
Find more information here: https://www.campusfrance.org/en/tuition-fees-France
Nancy?s high quality of life goes hand-in-hand with a low cost of living.
Why Nancy? --------------- Nancy is a center of excellence for NLP. It can boast of:
1/Two important research units: - LORIA, the computer science research laboratory http://www.loria.fr/en/ - ATILF the linguistic research laboratory http://www.atilf.fr/
Both research units participate in many national and international research projects. Their members are highly visible in the international community and regularly publish in the most prestigious conferences and journals of the field.
2/ Two Erasmus Mundus Masters funded by the European Union : - Erasmus Mundus Master Language and Communication Technology (LCT) http://lct-master.org/ - Erasmus Mundus Master EMLex. https://www.emlex.phil.fau.eu/
Why NLP? -------------- Natural Language Processing is a rapidly expanding field.
With the proliferation of digital data, there is a massive need for well-trained engineers and researchers able to exploit this data for commercial (for example: e-commerce, recommender systems and translation) and socio-political (for example: e-learning, opinion mining, and behavioural predictions) purposes.
Start-ups, small firms with strong R&D profiles and large companies (in France this includes Airbus, Thales, EDF, Orange, etc.) are looking for scientists and engineers who can contribute to the development of systems capable of managing and exploiting textual data.
The GAFAM (Google, Amazon, Facebook, Apple, Microsoft) also recruit intensively in both the NLP and the Speech processing domain.
There is also a strong need for Ph.D. candidates in the field, as demonstrated by the many Ph.D. proposals in both the public and the industrial domain.
Contacts ------------ Head of the Master program: Maxime Amblard maxime.amblard@univ-lorraine.fr Secretary: Karine Weisse karine.weisse@univ-lorraine.fr
Location of the courses: École des Mines (Artem Campus, Tram stop 'Blandan') http://www.alliance-artem.fr/
Application --------------- Non-European students --------------- Students from most non-UE countries shall apply on https://pastel.diplomatie.gouv.fr/etudesenfrance/dyn/public/authentification/login.html?_csrf=a530f0a6-e1d3-4052-ba91-f2cb8029fcd8&codeLangue=EN (search Traitement Automatique des Langues Nancy)
Students from other non-EU countries can directly apply with the following file: http://institut-sciences-digitales.fr/applicationfilem-sc_-nlp19-20/
European students --------------- The application file for EU students is available here: https://ecandidat.univ-lorraine.fr
Create an account then follow: - Offre de formation - Institut des sciences du digital, management et cognition - Master - M1-Traitement Automatique des Langues (TAL) (NANCY) (FI) or - M2-Traitement Automatique des Langues (TAL) PT Informatique, langue, parole et connaissances (NANCY) (FI)
More information: http://institut-sciences-digitales.fr/application-process-nlp-master-degree/#application
Academic Year 2019-2020 ----------------------------------- From 3rd to 13th September 2019: Refresher courses in mathematics (probability theory and statistics) and computer science (algorithms and programming).
M1: 1st semester from September 2nd to January 10th 2nd semester from January 13th to June 5th M2: 1st semester from September 2nd to March 27th Internship from February to August
In brief ---------- ? A program with strong national and international visibility ? Taught entirely in English ? A high quality research environment with two research centers covering a wide range of disciplines (linguistics, computer science, speech and language processing) and applications (human-machine interfaces, speech processing, knowledge extraction, statistical machine translation, etc.) ? Studies in a vibrant, human-size city
Course Description --------------------------- M1 Semester 1 --------------------------- PROBABILITIES, STATISTICS AND ALGORITHMS FOR AI ? Elementary mathematical tools, statistics, algorithms to define and solve artificial intelligence problems ? Case study approach towards mastering both theoretical and practical aspects of a topic
DESIGN AND ACQUISITION OF CORPORA ? Written corpora ? Speech corpora
SOFTWARE ENGINEERING ? Collecting, analysing and formalizing customers' needs ? Software design and development ? Functional analysis and specifications ? Project management
LINGUISTICS FOR NLP (1) ? Methods for Natural Language Processing ? Phonology ? Morphology
PROJECT AND FOREIGN LANGUAGE ? Hands-on project ? Foreign language (French)
--------------------------- M1 Semester 2 --------------------------- MACHINE LEARNING AND SEMANTIC WEB ? Machine learning theory ? Data mining ? Knowledge extraction
FORMAL TOOLS ? Logic ? Formal languages ? Calculability and complexity
DATA PROCESSING ? Storage and retrieval ? Data analysis
LINGUISTICS FOR NLP (2) ? Lexicology : lexical units and phraseology ? Syntax ? Semantics
PROJECT AND FOREIGN LANGUAGE ? Hands-on project ? Scientific communication ? Foreign language (French)
--------------------------- M2 Semester 1 --------------------------- DEEP LEARNING AND DATA MINING ? Neural Networks, Deep Neural Networks ? Data mining (structured data and text) ? Collaborative filtering
TEXT AND SPEECH PROCESSING ? Processing Textual Data ? Speech processing ? Terminology and ontology
NATURAL LANGUAGE PROCESSING AND DISCOURSE ? Application to text ? Computational semantics ? Discourse and Dialog modeling
LEXICONS AND GRAMMARS FOR NLP ? Diachronic and synchronic lexicology ? Lexical resources ? Syntactic framework
PROJECTS AND FOREIGN LANGUAGE ? Software project ? Law and ethics ? Research methods ? Professional integration ? Foreign language (French)
--------------------------- M2 Semester 2 --------------------------- Paid internship (company or research lab)
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4-6 | General chair and local arrangements chair of SIGDIAL 2020.
The SIGDIAL Exec seeks expressions of interest for the positions of general chair and local arrangements chair of SIGDIAL 2020.
GENERAL CHAIR
The general chair is responsible for schedule management, budget, communication, publicity, management of vendors not directly related to the local arrangements, and creation and maintenance of the conference website.
General chairs have typically attended SIGDIAL multiple times, and have some previous conference organization experience.
If you are interested in serving as general chair of SIGDIAL 2020, please email exec@sigdial.org with the following information:
- Name and affiliation
- Short statement of your interest in and qualifications for serving as general chair
- Previous experience with SIGDIAL: attendance, previous service positions, publications
- Previous experience with conference organization (for SIGDIAL or other conferences)
LOCAL ARRANGEMENTS CHAIR
The local arrangements chair is responsible for choosing and contracting with the conference venue for SIGDIAL, and also the Young Researchers? Roundtable on Spoken Dialog Systems (YRRSDS). They are also responsible for arranging/coordinating space and equipment, management of vendors related to local arrangements, catering and refreshments, organizing the banquet, and providing local information.
Local arrangement chairs have typically attended SIGDIAL at least once, and have some previous conference organization experience. Further, local arrangement chairs are usually based in the same local region as the conference itself. SIGDIAL 2020 will be (approximately) co-located with ACL 2020. ACL 2020 will be held in Seattle, USA, July 6-11 2020. SIGDIAL 2020 will be held immediately before or after ACL, also in Seattle, or somewhere readily accessible from Seattle. In the past, SIGDIAL has usually (but not always) been held at a university facility, rather than a private facility.
If you are interested in serving as local arrangements chair of SIGDIAL 2020, please email exec at exec@sigdial.org with the following information:
- Name and affiliation
- Short statement of your interest in and qualifications for serving as local chair
- Previous experience with SIGDIAL: attendance, previous service positions, publications
- Previous experience with conference organization (for SIGDIAL or other conferences)
- Proposed location and venue for SIGDIAL 2020, including description and statement of why it would be attractive for participants
SELECTION CRITERIA
In addition to self-nominations, the SIGDIAL Exec may nominate further candidates when selecting these roles. These roles will be selected considering a range of criteria, including previous experience with SIGDIAL, previous experience with conference management, and the representation of the organizing committee as a whole, including geography, industry/academia, gender, seniority, and other factors. The Exec may contact interested parties to request further information.
As in past years, the SIGDIAL Exec is responsible for selecting the SIGDIAL organizing committee.
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4-7 | CfProposals: 2021 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)
Call for Proposals
2021 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)
IEEE Signal Processing Society?s Machine Learning for Signal Processing Technical Committee (MLSP TC) is soliciting proposals from researchers interested in organizing the 2021 MLSP Workshop. The deadline for submission of proposals is July 15, 2019. The MLSP Workshop is a four-day event, and usually includes tutorials.
The proposals will be reviewed by MLSP TC and proposal finalists will be invited to present at MLSP 2019 be held from Oct 13-16 in Pittsburgh, PA, USA.
Proposing teams are asked to create a proposal that follows the following outline:
- Location and Venue - Give an idea on the venue size and facilities
- Conference Dates - Ensure they do not conflict with major holidays, or other SPS conferences and workshops. Typically the workshop is held during the period of mid September to mid October.
- Organizing Committee Members- Build the organizing committee considering the following representation: active SPS members; diversity including geographical, industry and academia, age, and gender; conference and/or workshop experience; Management experience
- Professional Conference Organizer (PCO) - Include brief summary about PCO and services they will provide
- Technical Program ? consider the overall structure and conference model; innovative initiatives; Student and Young Professional initiatives; and Industry-related initiatives
- Hotels
- Travel and Transportation
- Any other relevant information
Please submit your proposal to the MLSP TC Chair, Bhaskar Rao, at brao@ucsd.edu, by July 15, 2019 for consideration.
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4-8 | Call for bids eNTERFACE workshop 2020
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<table 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