Deadline for submission on March 4th
The 4th International Conference on Applications of Intelligent Systems,
APPIS 2022, will be held on 22-25 March 2022 in Las Palmas de Gran Canaria,
Spain. APPIS 2022 is organized by the University of Groningen, University of
Twente and the University of Las Palmas de Gran Canaria, and includes a
<http://appis.webhosting.rug.nl/2022/wismal-2022/> Winter School on Machine
Learning (WISMAL 2022).
APPIS 2022 aims at bringing together scientists who develop or apply
intelligent systems, machine learning, artificial intelligence, pattern
recognition, and related methods.
APPIS 2022 welcomes (but is not limited to) abstract contributions related
to the following topics:
* Images, videos and time-series analysis
* Machine learning and representation learning
* Statistical and structural pattern recognition
* Data visualization and dimensionality reduction
* Robotics
* Intelligent systems in health and medicine
* Cyber computing and security
* Bio-informatics
* Data mining
* Deep and reinforcement learning
* Cognitive discovery
* Algorithms for embedded and real-time systems
* Semantic technologies
* Intelligent buildings
* Intelligent sensors and sensor networks
* Augmented reality
* Adaptive systems
* Fuzzy systems
* Human-machine interaction
* Natural language processing
* Situation awareness systems
* Recommender systems
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APPIS 2022 accepts submissions of abstracts (1-2 pages in the ACM format).
Abstracts must be submitted electronically through the APPIS 2022 conference
web site in pdf format. Find more information in the submission page.
Each accepted contribution must be presented by one of the authors and
accompanied by at least one full registration fee payment.
Registration fee: 200 Euro, including participation in the Winter School on
Machine Learning, WISMAL 2022.
Since the number of participants is limited to 70, we recommend you to
register and secure your participation as soon as possible.
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Winter School on Machine Learning - WISMAL 2020
APPIS includes a short winter school consists of several tutorials that
present different techniques of Machine Learning. Please find more
information at the <http://appis.webhosting.rug.nl/2022/wismal-2022/>
WISMAL 2022 page.
The participation in the winter school is free of charge for registered
participants in APPIS 2022. The number of participants in the winter school
is limited to 70 and early registration is encouraged.
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Abstract paper due - 4 March 2022
Abstract acceptance notification - 8 March 2022
Early registration - 15 March 2022
Conference - 22-25 March 2022
Please find more information on the <http://appis.webhosting.rug.nl/2022/>
conference website.
the conference co-chairs
Nicolai Petkov
Nicola Strisciuglio
Carlos Travieso-Gonzalez
Hey Folks,
How are you doing today and I hope you are safe and well wherever you are.
My name is Omoleye Julius Fortunate and I'm a year three statistics student
of the federal University of Agriculture Abeokuta Ogun State Nigeria.
I would like to contribute to the Wikimedia ORES project for the 2022
google summer of code programme , and would really love to get an headstart
on how to get started.
I have skills using the Python Django framework, weak machine learning
skills , also some basic site reliability Engineering skills too.
I would be expecting a positive response from you soon.
Thank you
Omoleye Julius
Great opportunity in Computational Neuroscience: Assistant Professor in
Computational Neuroscience (W1) with tenure track option (to W2) at the
Institute for Cognitive Science, University of Osnabrück. (apologies for
multiple postings)
Full details at:
https://www.uni-osnabrueck.de/universitaet/stellenangebote/stellenangebote-…
---
This tenure-track Professorship is funded by the German Federal
Government and the Länder under its Program for the Promotion of Young
Academics (Tenure-Track Program). Upon meeting the general
administrative requirements, you will be employed as a civil servant on
limited tenure for an initial period of three years. If you receive a
positive evaluation after the initial three years, this period can be
extended by up to three further years. If you meet Osnabrück
University’s standards with respect to ability, competence and academic
achievement, you will be offered a tenured W2 Professorship in
accordance with the relevant legal provisions without further application.
Tasks and Responsibilities: Research and teaching should be in the area
of computational neuroscience. The professorship and research should be
focused on understanding principles of neuronal information processing
and cognitive processes. The professorship complements and cooperates
with the existing research groups of the Institute of Cognitive Science
at Osnabrück University. Further, the holder of the position should
actively contribute to existing and future collaborative research
projects (e.g. RTG Situated Cognition, RTG Computational Cognition) and
should participate in the respective continuation research proposal of
the Research Training Group Computational Cognition. In addition, the
professorship should engage in teaching in all degree programs of
Cognitive Science (BSc, MSc, PhD). Teaching is in English.
Conditions of Employment: Condition of employment is:
- Research experience in the field of neuronal information processing
with the focus of understanding principles of neuronal information
processing or cognitive processes.
- Research focus on at least one of the two following topics:
- Methods of Experimental cognitive neuroscience like EEG, MEG, fMRI,
TMS, cell recordings, eyetracking, or virtual reality
- Models of information processing in neurons, networks of neurons like
for example deep neuronal networks and machine learning.
- A coherent research profile, must be motivated by or explain
experimental data and there must be a realistic perspective to implement
all parts of the own research, including the experimental components or
data collection, at the university of Osnabrück.
- An excellent command of the English language.
- Willingness to participate in university administrative processes and
commission work. For non-German speaking applicants, willingness is
expected to learn the German language with a sufficient proficiency
within two years.
In addition, it would be welcome:
- Teaching experience in the field of the professorship
- Experience with acquiring third party research funding.
Legal Conditions of Employment: You will hold a first degree, have a
strong commitment to teaching, and have demonstrated your ability to
engage independently in advanced academic research, as a rule by
obtaining an outstanding PhD (in accordance with Section 30 subsection 2
of the Lower Saxony Higher Education Act [NHG]) in a relevant subject.
The time between the applicant’s final PhD examination or the completion
of other equivalent qualifications which qualify the applicant for the
position in accordance with Section 30 para. 2 sentence 1 No. 3 Lower
Saxony Higher Education Act and the applicant’s application for the
assistant professorship should be no longer than four years. This period
of time does not include periods caring for a child or several children
under 18 years of age or periods caring for a dependent relative and
increases by up to two years per child or period of care to a maximum
four years in multiple cases of care (Section 30 para. 5 Lower Saxony
Higher Education Act).
The position is available on a full-time or part-time basis.
Osnabrück University is a family-friendly university and is committed to
helping working/studying parents balance their family and working lives.
Osnabrück University is actively seeking to increase the number of
female Professors in its employ. Applications from women are therefore
particularly welcome. If two candidates are equally qualified,
preference will be given to the candidate with disability status.
For further information, please contact Prof. Dr Gordon Pipa, Tel. +49
541-969-2277, E-Mail: gpipa(a)uni-osnabrueck.de.
Please submit your application (including a resume with full details of
your scholarly and scientific employment history, list of publications
and courses taught as well as planned research) in electronic form (as
one pdf file) together with the “Bewerbungsprofil” [“Applicant Profile”]
(DOCX, 13,58 kB) to the Dean of the School of Human Sciences, Prof. Dr.
Susanne Boshammer, Universität Osnabrück, 49069 Osnabrück
(bewerbungfb08(a)uni-osnabrueck.de) to arrive by March 27, 2022. Please
enter the code word "CNtt" in the subject of your e-mail.
We look forward to receiving your application.
--
Prof. Dr. Peter König, Institute of Cognitive Science, University Osnabrück
--
Prof. Dr. Peter König, Institute of Cognitive Science, University Osnabrück
Hello,
I'm Sahethi DG, a third-year undergraduate studying Computer Engineering at
VJTI, Mumbai, India. I have been going through the codebase of ORES for a
few days now, figuring my way and trying to contribute. I am currently
working on some issues and I look forward to being a part of the community
and collaborating on this project.
I was wondering if there was a dedicated Slack or IRC for this project
because I can't seem to find anyone active on Libera Chat. I referred to
the links mentioned in "Get in touch" for ORES here
https://www.mediawiki.org/wiki/New_Developers, please do send me an invite
if possible.
Best regards,
Sahethi
Dear Colleagues,
** Apologies for cross-posting **
This is Michal Ptaszynski from Kitami Institute of Technology, Japan.
We are accepting papers for the Information Processing & Management (IP&M) (IF: 6.222) journal Special Issue on Science Behind Neural Language Models. This special issue is also a Thematic Track at Information Processing & Management Conference 2022 (IP&MC2022), meaning, that at least one author of the accepted manuscript will need to attend the IP&MC2022 conference.
For more information about IP&MC2022, please visit:
https://www.elsevier.com/events/conferences/information-processing-and-mana…
The deadline for manuscript submission is June 15, 2022, but your paper will be reviewed immediately after submission and will be published as soon as it is accepted.
We hope you will consider submitting your paper.
https://www.elsevier.com/events/conferences/information-processing-and-mana…
Info regarding submission:
https://www.elsevier.com/events/conferences/information-processing-and-mana…
Best regards,
Michal PTASZYNSKI, Ph.D., Associate Professor
Department of Computer Science
Kitami Institute of Technology,
165 Koen-cho, Kitami, 090-8507, Japan
TEL/FAX: +81-157-26-9327
michal(a)mail.kitami-it.ac.jp
============================================
Information Processing & Management (IP&M) (IF: 6.222)
Special Issue on "Science Behind Neural Language Models"
&
Information Processing & Management Conference 2022 (IP&MC2022)
Thematic Track on "Science Behind Neural Language Models"
Motivation
The last several years showed explosive popularity of neural language models, especially large pre-trained language models based on the transformer architecture. The field of Natural Language Processing (NLP) and Computational Linguistics (CL) experienced a shift from simple language models such as Bag-of-Words, and word representations like word2vec, or GloVe, to more contextually-aware language models, such as ELMo, or more recently, BERT, or GPT including their improvements and derivatives. The general high performance obtained by BERT-based models in various tasks even convinced Google to apply it as a default backbone in its search engine query expansion module, thus making BERT-based models a mainstream, and a strong baseline in NLP/CL research. The popularity of large pretrained language models also allowed a major growth of companies providing freely available repositories of such models, and, more recently, the founding of Stanford University’s Center for Research on Foundation Models (CRFM).
However, despite the overwhelming popularity, and undeniable performance of large pretrained language models, or “foundation models”, the specific inner-workings of those models have been notoriously difficult to analyze and the causes of - usually unexpected and unreasonable - errors they make, difficult to untangle and mitigate. As the neural language models keep gaining in popularity while expanding into the area of multimodality by incorporating visual and speech information, it has become the more important to thoroughly analyze, fully explain and understand the internal mechanisms of neural language models. In other words, the science behind neural language models needs to be developed.
Aims and scope
With the above background in mind, we propose the following Information Processing & Management Conference 2022 (IP&MC2022) Thematic Track and Information Processing & Management Journal Special Issue on Science Behind Neural Language Models.
The TT/SI will focus on topics deepening the knowledge on how the neural language models work. Therefore, instead of taking up basic topics from the fields of CL and NLP, such as improvement of part-of-speech tagging, or standard sentiment analysis, regardless of whether they apply neural language models in practice, we will focus on promoting research that specifically aims at analyzing and understanding the “bells and whistles” of neural language models, for which the generally perceived science has not been established yet.
Target audience
The TT/SI will aim at the audience of scientists, researchers, scholars, and students performing research on the analysis of pretrained language models, with a specific focus on explainable approaches to language models, analysis of errors such models make, methods for debiasing, detoxification and other methods of improvement of the pretrained language models.
The TT/SI will not accept research on basic NLP/CL topics for which the field has been well established, such as improvement of part-of-speech tagging, sentiment analysis, etc., even if they apply neural language models unless they directly contribute to furthering the understanding and explanation of the inner workings of large scale pretrained language models.
List of Topics
List of Topics
The Thematic Track / Special Issue will invite papers on topics listed, but not limited to the following:
- Neural language model architectures
- Improvement of neural language model generation process
- Methods for fine tuning and optimization of neural language models
- Debiasing neural language models
- Detoxification of neural language models
- Error analysis and probing of neural language models
- Explainable methods for neural language models
- Neural language models and linguistic phenomena
- Lottery Ticket Hypothesis for neural language models
- Multimodality in neural language models
- Generative neural language models
- Inferential neural language models
- Cross-lingual or multilingual neural language models
- Compression of neural language models
- Domain specific neural language models
- Expansion of information embedded in neural language models
Important Dates:
Thematic track manuscript submission due date; authors are welcome to submit early as reviews will be rolling: June 15, 2022
Author notification: July 31, 2022
IP&MC conference presentation and feedback: October 20-23, 2022
Post conference revision due date: January 1, 2023
Submission Guidelines:
Submit your manuscript to the Special Issue category (VSI: IPMC2022 HCICTS) through the online submission system of Information Processing & Management.
https://www.editorialmanager.com/ipm/
Authors will prepare the submission following the Guide for Authors on IP&M journal at (https://www.elsevier.com/journals/information-processing-and-management/030…). All papers will be peer-reviewed following the IP&MC2022 reviewing procedures.
The authors of accepted papers will be obligated to participate in IP&MC 2022 and present the paper to the community to receive feedback. The accepted papers will be invited for revision after receiving feedback on the IP&MC 2022 conference. The submissions will be given premium handling at IP&M following its peer-review procedure and, (if accepted), published in IP&M as full journal articles, with also an option for a short conference version at IP&MC2022.
Please see this infographic for the manuscript flow:
https://www.elsevier.com/__data/assets/pdf_file/0003/1211934/IPMC2022Timeli…
For more information about IP&MC2022, please visit https://www.elsevier.com/events/conferences/information-processing-and-mana….
Thematic Track / Special Issue Editors:
Managing Guest Editor:
Michal Ptaszynski (Kitami Institute of Technology)
Guest Editors:
Rafal Rzepka (Hokkaido University)
Anna Rogers (University of Copenhagen)
Karol Nowakowski (Tohoku University of Community Service and Science)
For further information, please feel free to contact Michal Ptaszynski directly.
I am pleased to share with you a new proposal for a Wikimedia project: Wikianswers. The project intends to integrate artificial intelligence question-answering systems with a wiki question-and-answer platform.
Like similar projects, users will receive one or more answers to their questions from artificial intelligence question-answering systems, but, unlike other projects, they will subsequently be able to edit those answers, as well as any provided explanations and argumentation, per wiki technology. User-corrected content could later be utilized to retrain the question-answering systems.
The proposed project aims to provide value as a new resource for users and as training data for multiple artificial intelligence systems.
While the proposed project can provide users with answers across various question domains, a scenario of interest to me is that of moral question answering. I hope that this scenario is also of some interest to you.
The hyperlink to the project proposal: https://meta.wikimedia.org/wiki/Wikianswers .
Thank you for any ideas, comments, questions, or suggestions with which to improve the project proposal. Thank you for taking the time to express your support of or opposition to the proposed new project.
Best regards,
Adam Sobieski
http://www.phoster.com
Dear Colleague,
we are writing to you as we understand you may be interested in this
year’s edition of the EAI International Conference *“AI for People:
Towards Sustainable AI” (CAIP’21)*.
Below you will find the official Call for Full papers.
Please feel free to distribute it to mailing lists you manage and to
everybody who may be interested.
Thank you and we hope to see you in *CAIP’21*!
------------------------------------------------
*International Conference “AI for People: Towards Sustainable AI” (CAIP’21)*
November 20-24, 2021
https://aiforpeople.org/conference
*Call for Full Papers*
https://aiforpeople.org/conference/cfp.php
Please distribute
(Apologies for cross-posting)
If you wish to receive more information about CAIP’21
https://mailchi.mp/281085ba7b4b/caip21
CAIP'21 is technically sponsored by: *European Alliance for Innovation
(EAI)*.
CAIP'21 is supported by: *Technology Innovation Institute (TII)*,
*European Association for Artificial Intelligence (EurAI)*.
CAIP'21 partners: Encode Justice, The Future Society (TFS), MLDawn
------------------------------------------------
The International Conference “AI for People: Towards Sustainable AI” was
born out of the idea of shaping Artificial Intelligence technology
around human and societal needs. While Artificial Intelligence (AI) can
be a beneficial tool, its development and its deployment impact society
and the environment in ways that need to be thoroughly addressed and
confronted.
This year’s edition will focus on Sustainable AI, covering different
aspects of social development, environmental protection, and economic
growth applied in the design and deployment of AI systems. The
conference will provide its participants with opportunities to gain a
better understanding of the major challenges of utilizing AI for the
societal good. Additionally, it should serve as an incubator for
interdisciplinary communities that share a research agenda to exchange
and discuss ideas related to the design and application of Sustainable
AI. Here, Sustainable AI is a movement to foster change towards greater
ecological integrity and social justice in the entire life cycle of AI
systems.
**** Themes and Topics ****
The conference will be interdisciplinary and it welcomes contributions
from different disciplines, spanning from computer science, the social
sciences, and the humanities.
Possible topics include but are not limited to:
- AI applications for the social good and towards sustainable
development goals
- Ethics of Artificial Intelligence
- Sustainable AI for Smart Cities
- Policy recommendations for Sustainable AI
- Green AI for environmental protection
- Accuracy and Robustness of AI systems
- Bias and Fairness in AI Systems
- Privacy and Accountability in AI Systems
- Safety and Security in AI Systems
- Explainability and Transparency in AI Systems
**** Important Dates ****
Submission deadline: October 1st, 2021
Notification: November 1st, 2021
Camera-ready: November 6th, 2021
Conference Days: November 20-24, 2021
**** EAI Proceedings ****
Conference proceedings will be published in the EAI CORE Proceedings and
included in the European Digital Library (EUDL) and will be submitted
for inclusion in leading indexing services, including Ei Compendex, ISI
Web of Science, Scopus, CrossRef, Google Scholar, DBLP.
**** Keynote Speakers ****
CAIP’21 will host international keynote speakers (to be completed):
- Priya Donti, Chair at Climate Change AI
- Sneha Revanur, Founder of Encode Justice
- Nicolas Miailhe, President of The Future Society
- Maria De-Arteaga, Assistant Prof. at University Texas
**** Organising Committee ****
The International Conference on “AI for People: Towards Sustainable AI”
is organized by the nonprofit international organization “AI for People”
(aiforpeople.org).
/General Chairs/
- Marta Ziosi (Oxford Internet Institute, University Oxford)
- Philipp Wicke (University College, Dublin)
- João Miguel Cunha (University of Coimbra)
- Angelo Trotta (University of Bologna)
/Program Chairs/
- Lea Buchhorn (Leiden University)
- Vincenzo Lomonaco (University of Pisa)
/Finance Chair/
- Aina Turillazzi (Tilburg University)
/Publication Chair/
- Angelo Trotta (University of Bologna)
TPC Chair
- Kevin Trebing (Plan D GmbH)
- Gabriele Graffieti (University of Bologna)
/Technical Program Committee (to be completed)/
- Adam Poulsen – Charles Sturt University
- Aiste Gerybaite – University of Bologna
- Andrea Cossu – Scuola Normale Superiore
- Aníbal Monasterio Astobiza – IFS-CSIC
- Christoph Lütge – Technical University of Munich
- Clàudia Figueras – Stockholm University
- Claudio Gallicchio – University of Pisa
- Daniel Schiff – Georgia Institute of Technology
- Federico Montori – University of Bologna
- Giovanni Sartor – EUI/CIRSFID
- Jake Goldenfein – University of Melbourne
- Jakob Mökander – Oxford Internet Institute, University of Oxford.
- Jason Borenstein – Georgia Institute of Technology
- Keng Siau – City University of Hong Kong
- Laurynas Adomaitis – NordSec
- Lorenzo Pellegrini – University of Bologna
- Luca Bedogni – University of Modena and Reggio Emilia
- Maria Celia Fernández Aller – Technical University of Madrid
- Maria Milossi – University of Macedonia
- Marianna Ganapini – Union College
- Marija Slavkovik – University of Bergen
- Michele Loi – University of Zurich
- Pradeep Murukannaiah – TU Delft, NL
- Rajitha Ramanayake – University College Dublin
- Seth D. Baum – Global Catastrophic Risk Institute
- Stefan Sarkadi – INRIA, France
*Date:* Thursday, 7 October 2021
*Location*: Virtual, co-located with AKBC 2021
*Contact Email: *cskb-akbc21(a)googlegroups.com
*Website:* http://akbc-cskb.github.io/
Recent advances in large pre-trained language models have shown that
machines can directly learn large quantities of commonsense knowledge
through self-supervised learning on raw text. However, they still fall
short of human-like understanding capabilities: they make inconsistent
predictions, learn to exploit spurious patterns, and fail to robustly apply
learned knowledge to downstream applications. Consequently, the development
and integration of unpaired, outside knowledge representation sources
remains critically important to provide machine commonsense engines with
scaffolding to learn structured reasoning. We organize this workshop to
encourage discussion of current progress on building machines with
commonsense knowledge and reasoning abilities, with special focus on
commonsense knowledge bases (CSKBs).
*** Topics of Interest ***
Topics of interest include, but are not limited to:
* Resources: acquiring commonsense knowledge (from text corpora, images,
videos, pre-trained neural models, etc.); constructing and completing
(semi-)structured CSKBs; consolidating CSKBs under unified schemas.
* Benchmarks: designing challenging tasks and building datasets to evaluate
models' commonsense knowledge and reasoning abilities; designing new
evaluation schemas and metrics for commonsense reasoning tasks,
particularly for open-ended and generative tasks.
* Methods: methods for commonsense reasoning tasks; methods that integrate
CSKBs and neural models; methods that use CSKBs to improve the
interpretability and explainability of neural models for commonsense
reasoning and more.
* Analysis: methods to probe commonsense knowledge from NLP models; methods
to understand reasoning mechanisms of existing methods; methods that
identify limitations of existing methods for AI applications (including but
not limited to NLP, CV and robotics) due to lack of commonsense knowledge.
*** Submission Information ***
Papers should be submitted in OpenReview:
https://openreview.net/group?id=AKBC.ws/2021/Workshop/CSKB.
We solicit two categories of papers:
* Workshop papers: describing new, previously unpublished research in this
field. The submissions should follow the AKBC 2021 style guidelines:
https://github.com/akbc-conference/style-files/blob/master/akbc-latex.zi...
and contain up to 10 pages, excluding references and appendices (which
should be put after references). Submissions will be subject to a
single-blind review process (i.e. they need not be anonymized). Final
versions of accepted papers will be allowed 1 additional page of content so
that reviewer comments can be taken into account.
* Papers on topics relevant to the workshop theme, previously published at
NLP or ML conferences. These papers can be submitted in their original
format. Submissions will be reviewed for fit to the workshop topics.
In both categories, accepted papers will be published on the workshop
website (note that the workshop is non-archival), and will be presented at
the workshop either as a talk or a poster.
*** Important Dates ***
* All paper submissions due – August 5, 2021
* Notification of acceptance – Sep 10, 2021
* Camera-ready papers due – September 30, 2021
* Workshop – October 7, 2021
*** CSKB 2021 Keynote Speakers ***
We’re excited to have the following keynote speakers at CSKB 2021:
* Rachel Rudinger, University of Maryland
* Sara Hooker, Google Brain
* Maarten Sap, Allen Institute for AI (AI2) and CMU
* Xiang Ren, University of Southern California
We will also be holding a panel discussion with the invited speakers as
well as the following panelists:
* Mohit Bansal, University of North Carolina at Chapel Hill
* Greg Durrett, UT Austin
* Sameer Singh, UC Irvine
* Aida Nematzadeh, DeepMind
*** Organizing Committee ***
Vered Shwartz, Allen Institute for AI (AI2), University of Washington, and
University of British Columbia
Antoine Bosselut, Stanford University and EPFL
Xiang Lorraine Li, UMass Amherst
Bill Yuchen Lin, University of Southern California