Dear colleagues,
We are happy to invite you to join the *Arabic NER SharedTask 2023*
<https://dlnlp.ai/st/wojood/> which will be organized as part of the WANLP
2023. We will provide you with a large corpus and Google Colab notebooks to
help you reproduce the baseline results.
دعوة للمشاركة في مسابقة استخراج الكيونات المسماه من النصوص العربية. سنزود
المشاركين بمدونة وبرمجيات للحصول على نتائج مرجعية يمكنهم البناء عليها.
*INTRODUCTION*
Named Entity Recognition (NER) is integral to many NLP applications. It is
the task of identifying named entity mentions in unstructured text and
classifying them to predefined classes such as person, organization,
location, or date. Due to the scarcity of Arabic resources, most of the
research on Arabic NER focuses on flat entities and addresses a limited
number of entity types (person, organization, and location). The goal of
this shared task is to alleviate this bottleneck by providing Wojood, a
large and rich Arabic NER corpus. Wojood consists of about 550K tokens (MSA
and dialect, in multiple domains) that are manually annotated with 21
entity types.
*REGISTRATION*
Participants need to register via this form (
*https://forms.gle/UCCrVNZ2LaPviCZS6* <https://forms.gle/UCCrVNZ2LaPviCZS6>).
Participating teams will be provided with common training development
datasets. No external manually labelled datasets are allowed. Blind test
data set will be used to evaluate the output of the participating teams.
Each team is allowed a maximum of 3 submissions. All teams are required to
report on the development and test sets (after results are announced) in
their write-ups.
*FAQ*
For any questions related to this task, please check our *Frequently Asked
Questions*
<https://docs.google.com/document/d/1XE2n89mFLic2P9DO_sAD51vy734BOt0kgtZ6bFf…>
*IMPORTANT DATES*
- March 03, 2023: Registration available
- May 25, 2023: Data-sharing and evaluation on development set
Avaliable
- June 10, 2023 June 30, 2023: Registration deadline (Extended)
- July 20, 2023: Test set made available
- July 30, 2023: Evaluation on test set (TEST) deadline
- Augest 29, 2023: Shared task system paper submissions due
- October 12, 2023: Notification of acceptance
- October 30, 2023: Camera-ready version
- TBA: WANLP 2023 Conference.
* All deadlines are 11:59 PM UTC-12:00 (Anywhere On Earth).
** All deadlines are 11:59 PM UTC-12:00 (Anywhere On Earth).*
*CONTACT*
For any questions related to this task, please contact the organizers
directly using the following email address: *NERShare...(a)gmail.com
<https://groups.google.com/>* or join the google group:
*https://groups.google.com/g/ner_sharedtask2023*
<https://groups.google.com/g/ner_sharedtask2023>.
*SHARED TASK*
As described, this shared task targets both flat and nested Arabic NER. The
subtasks are:
*Subtask 1:* *Flat NER*
In this subtask, we provide the Wojood-Flat train (70%) and development
(10%) datasets. The final evaluation will be on the test set (20%). The
flat NER dataset is the same as the nested NER dataset in terms of
train/test/dev split and each split contains the same content. The only
difference in the flat NER is each token is assigned one tag, which is the
first high-level tag assigned to each token in the nested NER dataset.
*Subtask 2:* *Nestd NER*
In this subtask, we provide the Wojood-Nested train (70%) and development
(10%) datasets. The final evaluation will be on the test set (20%).
*METRICS*
The evaluation metrics will include precision, recall, F1-score. However,
our official metric will be the micro F1-score.
The evaluation of shared tasks will be hosted through CODALAB. Teams will
be provided with a CODALAB link for each shared task.
-*CODALAB link for NER Shared Task Subtask 1 (Flat NER)*
<https://codalab.lisn.upsaclay.fr/competitions/11594>
-*CODALAB link for NER Shared Task Subtask 2 (Nestd NER)*
<https://dlnlp.ai/st/wojood/>
*BASELINES*
Two baseline models trained on Wojood (flat and nested) are provided:
*Nested NER baseline:* is presented in this *article*
<https://aclanthology.org/2022.lrec-1.387/>, and code is available in
*GitHub* <https://github.com/SinaLab/ArabicNER>. The model achieves a micro
F1-score of 0.9059 (note that this baseline does not handle nested entities
of the same type).
*Flat NER baseline:* same code repository for nested NER (*GitHub*
<https://github.com/SinaLab/ArabicNER>) can also be used to train flat NER
task. Our flat NER baseline achieved a micro F1-score of 0.8785.
*GOOGLE COLAB NOTEBOOKS*
To allow you to experiment with the baseline, we authored four Google Colab
notebooks that demonstrate how to train and evaluate our baseline models.
[1] *Train Flat NER*
<https://gist.github.com/mohammedkhalilia/72c3261734d7715094089bdf4de74b4a>:
This notebook can be used to train our ArabicNER model on the flat NER task
using the sample Wojood data found in our repository.
[2] *Evaluate Flat NER*
<https://gist.github.com/mohammedkhalilia/c807eb1ccb15416b187c32a362001665>:
this notebook will use the trained model saved from the notebook above to
perform evaluation on unseen dataset.
[3] *Train Nested NER*
<https://gist.github.com/mohammedkhalilia/a4d83d4e43682d1efcdf299d41beb3da>:
This notebook can be used to train our ArabicNER model on the nested NER
task using the sample Wojood data found in our repository.
[4] *Evaluate Nested NER*
<https://gist.github.com/mohammedkhalilia/9134510aa2684464f57de7934c97138b>:
this notebook will use the trained model saved from the notebook above to
perform evaluation on unseen dataset.
*ORGANIZERS*
- Mustafa Jarrar, Birzeit University
- Muhammad Abdul-Mageed, University of British Columbia & MBZUAI
- Mohammed Khalilia, Birzeit University
- Bashar Talafha, University of British Columbia
- AbdelRahim Elmadany, University of British Columbia
- Nagham Hamad, Birzeit University
- Alaa Omer, Birzeit University
[Apologies for multiple postings]
We are happy to announce that 1 new written corpus is now available in
our catalogue.
*Archives of "El Mundo" Newspaper – Years 2020-2022
<http://catalog.elra.info/en-us/repository/browse/ELRA-W0332/>*
ISLRN: 124-545-396-179-3 <http://www.islrn.org/resources/124-545-396-179-3>
This corpus consists of 45,658 articles in Spanish from electronic
archives of "El Mundo" Newspaper between 2020 and 2022. A few articles
also come from publications from other related media: El Mundo Alicante,
El Mundo Andalucía, El Mundo Baleares, El Mundo Catalunya, El Mundo
Valéncia et Expansión. The number of articles available per year is as
follows:
- 2020: 15,073 articles
- 2021: 14,461 articles
- 2022: 16,124 articles
TOTAL: 45,658 articles
All articles are provided in text format, including HTML tags.
This data is released thanks to Unidad Editorial Información General,
S.L.U., Spain.
This corpus may be also obtained as separate years as follows:
Archives of "El Mundo" Newspaper – Year 2020
<http://catalog.elra.info/en-us/repository/browse/ELRA-W0333/>
Archives of "El Mundo" Newspaper – Year 2021
<http://catalog.elra.info/en-us/repository/browse/ELRA-W0334/>
Archives of "El Mundo" Newspaper – Year 2022
<http://catalog.elra.info/en-us/repository/browse/ELRA-W0335/>
For more information on the catalogue or if you would like to enquire
about having your resources distributed by ELRA, please *contact us*
<mailto:contact@elda.org>.
_________________________________________
Visit the *ELRA Catalogue of Language Resources* <http://catalog.elra.info>
Visit the *Universal Catalogue* <http://universal.elra.info>**
*Archives *
<http://www.elra.info/en/catalogues/language-resources-announcements>of
ELRA Language Resources Catalogue Updates
/Our apologies if you have received multiple copies of this announcement./
*Post Title:* *PhD Studentship in Causal Machine Learning for Multi-modal
data in NLP/Healthcare*
*Location: ADAPT Centre, MTU, Cork Campus, Ireland *
*Anticipated Start Date: **September, 2023*
*Closing Date:* *27 June, 2023*
We are seeking highly motivated and talented individual to join our
research team as PhD candidate. This is a full-time, fully-funded position
that offers the opportunity to work on innovative projects and make a
significant contribution at the interface between machine learning/deep
learning, healthcare and Natural Language Processing (NLP) with potential
research direction in one of the following areas: Causal reasoning for
multi-modal
generation, Causal discovery from multi-modal data, Causal reasoning
for multi-modal
decision making, Causal inference across modalities and Evaluation metrics
for multi-modal causal learning. However, we are open to align the Ph.D.
research project with your individual interests and expertise. The specific
focus and trajectory of the research will be influenced by your personal
preferences and research objectives. Your unique perspective and ideas are
highly encouraged and valued, as they will contribute to shaping the
research project. The successful candidate will be hosted at ADAPT *Centre
@ MTU <Centre@MTU>*, Ireland and closely work with a team of mentors from
academia and industry.
*Why ADAPT Centre?*
-
Contribute to the ADAPT research agenda that pioneers and combines
research in AI driven technologies: Natural Language Processing,
Video/Text/Image/Speech processing, digital engagement & HCI, semantic
modeling, personalisation, privacy & data governance.
-
Work with our interdisciplinary team of leading experts from the
complementary fields of, Social Sciences, Communications, Commerce/Fintech,
Ethics, Law, Health, Environment and Sustainability.
-
Leverage our success. ADAPT’s researchers have signed 43 collaborative
research projects, 52 licence agreements and oversee 16 active
commercialisation funds and 52 commercialisation awards. ADAPT has won 40
competitive EU research projects and obtained €18.5 million in
non-exchequer non-commercial funding. Additionally, six spinout companies
have been formed. ADAPT’s researchers have produced over 1,500 journal and
conference publications and nearly 100 PhD students have been trained.
As an ADAPT funded PhD researcher you will have access to a network of 85
global experts and over 250 staff as well as a wide multi-disciplinary
ecosystem across 8 leading Irish universities. We can influence and inform
your work, share our networks and collaborate with you to increase your
impact, and accelerate your career opportunities. Specifically we offer:
1.
Opportunity to build your profile at international conferences and
global events.
2.
A solid career pathway through formalised training & development, expert
one-on-one supervision and exposure to top specialists.
3.
A Fully funded, 4 year PhD postgraduate studentship which includes a
tax-free stipend of approx. €18,500 per year for up to four years including
tuition fees, research and equipment costs and all costs associated with
training related covered.
*Minimum qualifications*
-
Master’s degree in either Natural Language Processing, Artificial
Intelligence, Machine Learning, Data Science, Computer Science, Computer
Engineering, Electrical and Electronic Engineering or related disciplines
with strong programming skills.
-
Expertise and interest in Machine Learning/Natural Language
Processing/Causal
Machine Learning
-
Previous scientific publication experience preferred.
-
Excellent written and verbal communication and interpersonal skills
*Application Process **(incomplete application will not be considered)*
Interested candidates can send an application with the following documents
directly to Mohammed Hasanuzzaman (*mohammed.hasanuzzaman(a)adaptcentre.ie*
<mohammed.hasanuzzaman(a)adaptcentre.ie>)
1.
Detailed curriculum vitae, including – if applicable – relevant
publications;
2.
Transcripts of degrees,
3.
The name and email contacts of two academic referees,
4.
A cover letter/letter of introduction (max 2000 words). In the letter,
applicants should include the following details:
1.
An explanation of your interest in the research to be conducted and
why you believe they are suitable for the position.
2.
Details of your final year undergraduate project (if applicable)
3.
Details of your MSc project (if applicable)
4.
Details of any relevant modules previously taken, at undergraduate
and/or Master level.
5.
Details of any relevant work experience (if applicable)
------------------------------------------------------------------------------------------------------
*Dr. Mohammed Hasanuzzaman, Lecturer, Munster Technological University
<https://www.mtu.ie/> *
*Funded Investigator, ADAPT Centre- <https://www.adaptcentre.ie/> A
<https://www.adaptcentre.ie/>* World-Leading SFI Research Centre
<https://www.adaptcentre.ie/>
*Member, Lero, the SFI Research Centre for Software
<https://lero.ie/>**C**hercheur
Associé*, GREYC UMR CNRS 6072 Research Centre, France
<https://www.greyc.fr/en/home/>
*Associate Editor:** IEEE Transactions on Affective Computing, Nature
Scientific Reports, IEEE Transactions on Computational Social Systems, ACM
TALLIP, PLOS One, Computer Speech and Language*
Dept. of CS
Munster Technological University
Bishopstown campus
Cork e: mohammed.hasanuzzaman(a)adaptcentre.ie <email(a)adaptcentre.ie>/
Ireland https://mohammedhasanuzzaman.github.io/
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13/06/23,
13:09:55
Deadline extension: 3rd Workshop on Computational Linguistics for the Political and Social Sciences (CPSS 2023): https://sites.google.com/view/cpss2023konvens/home-page
* Workshop description *
This workshop aims at bringing together researchers and ideas from computational linguistics/NLP and the text-as-data community from political and social science to foster collaboration and catalyze further interdisciplinary research efforts between these communities.
* Potential topics *
- Modeling political communication with NLP (e.g. topic classification, position measurement)
- Mining policy debates from heterogeneous textual sources
- Modeling complex social constructs (e.g. populism, polarization, identity) with NLP methods
- Political and social bias in language models
- Methodological insights in interdisciplinary collaboration: workflows, challenges, best practices
- Application of NLP methods to understand and support democratic decision making
- Resources and tools for Political/Social Science research
- … and more
* Important dates *
- Submission deadline: June 24, 2023
- Notification of acceptance: July 18, 2023
- Camera-ready deadline: July 22, 2023
- Workshop: September 22, 2023
The workshop is co-located with KONVENS 2023 in Ingolstadt (https://www.thi.de/konvens-2023).
* Submissions *
We solicit two types of submissions:
- archival papers describing original and unpublished work (long papers: max. 8 pages, references/appendix excluded; short papers: max 4 pages, references/appendix excluded). Accepted papers will be published in the ACL anthology. For the submission format, refer to the KONVENS template.
- non-archival papers (1-page abstracts, references excluded) describing already published research or ongoing work
The two formats will meet the need of researchers from different communities, allowing the exchange of ideas in a "get to know each other" environment which we hope will foster future collaborations.
For more information, please refer to the workshop website: https://sites.google.com/view/cpss2023konvens/home-page
If you have any questions, please feel free to contact the workshop organizers.
* Organizers *
Gabriella Lapesa (U-Stuttgart)
Christopher Klamm (U-Mannheim)
Theresa Gessler (European University Viadrina)
Valentin Gold (U-Göttingen)
Simone Ponzetto (U-Mannheim)
Application links
*Netherlands*
<https://jobs.lever.co/veeva/a6c967ac-5bbb-412b-9c3d-b72d709b8da7> [
https://jobs.lever.co/veeva/a6c967ac-5bbb-412b-9c3d-b72d709b8da7]
*Germany
<https://jobs.lever.co/veeva/e73b2147-5e3c-41cf-8f9e-db64dcdd1d3a> *[
https://jobs.lever.co/veeva/e73b2147-5e3c-41cf-8f9e-db64dcdd1d3a]
Linkedin: https://www.linkedin.com/posts/activity-7061693573410254848-wJ6R
*What You'll Do*
- Adopt the latest technologies and trends in NLP to your platform
- Experience with training, fine-tuning, and serving Large Language
Models
- Design, develop, and implement an end-to-end pipeline for extracting
predefined categories of information from large-scale, unstructured data
across multi-domain and multilingual settings
- Create a robust semantic search functionality that effectively answers
user queries related to various aspects of the data
- Use and develop named entity recognition, entity-linking,
slot-filling, few-shot learning, active learning, question/answering, dense
passage retrieval, and other statistical techniques and models for
information extraction and machine reading
- Deeply understand and analyze our data model per data source and
geo-region and interpret model decisions
- Collaborate with data quality teams to define annotation tasks and
metrics and perform a qualitative and quantitative evaluation. We have more
than 1900 curators!
- Utilize cloud infrastructure for model development, ensuring seamless
collaboration with our team of software developers and DevOps engineers for
efficient deployment to production
*Requirements*
- 4+ years of experience as a data scientist (or 2+ years with a Ph.D.
degree)
- Master's or Ph.D. in Computer Science, Artificial Intelligence,
Computational Linguistics, or a related field
- Strong theoretical knowledge of Natural Language Processing, Machine
Learning, and Deep Learning techniques
- Proven experience working with large language models and transformer
architectures, such as GPT, BERT, or similar
- Familiarity with large-scale data processing and analysis, preferably
within the medical domain
- Proficiency in Python and relevant NLP libraries (e.g., NLTK, SpaCy,
Hugging Face Transformers)
- Experience in at least one framework for BigData (e.g., Ray, Spark)
and one framework for Deep Learning (e.g., PyTorch, JAX)
- Experience working with cloud infrastructure (e.g., AWS, GCP, Azure)
and containerization technologies (e.g., Docker, Kubernetes) and
experience with bashing script
- Strong collaboration and communication skills, with the ability to
work effectively in a cross-functional team
- Used to start-up environments
- Social competence and a team player
- High energy and ambitious
- Agile mindset
*You can work remotely anywhere in Germany or The Netherlands, but you have
to live in Germany or The Netherlands and be legally authorized to work
there without requiring Veeva's support for a visa or relocation. If you do
not meet this condition but you think you are an exceptional candidate,
please clarify it in a separate note, and we will consider it.About
Link: Our product offers real-time academic, social, and medical data to
build comprehensive profiles. These profiles help our life-science industry
partners find the right experts to accelerate the development and adoption
of new therapeutics. We accelerate clinical trials and equitable care. We
are proud that our work helps patients receive their most urgent care
sooner.*
*About Veeva:* Veeva is a mission-driven organization that aspires to help
our customers in Life Sciences and Regulated industries bring their
products to market, faster. We are shaped by our values: Do the Right
Thing, Customer Success, Employee Success, and Speed. Our teams develop
transformative cloud software, services, consulting, and data to make our
customers more efficient and effective in everything they do. Veeva is a
work anywhere company. You can work at home, at a customer site, or in an
office on any given day. As a Public Benefit Corporation, you will also
work for a company focused on making a positive impact on its customers,
employees, and communities.
Application links
*Netherlands*
<https://jobs.lever.co/veeva/a6c967ac-5bbb-412b-9c3d-b72d709b8da7> [
https://jobs.lever.co/veeva/a6c967ac-5bbb-412b-9c3d-b72d709b8da7]
*Germany
<https://jobs.lever.co/veeva/e73b2147-5e3c-41cf-8f9e-db64dcdd1d3a> *[
https://jobs.lever.co/veeva/e73b2147-5e3c-41cf-8f9e-db64dcdd1d3a]
Linkedin: https://www.linkedin.com/posts/activity-7061693573410254848-wJ6R
Ehsan Khoddam
Data Science Manager at Veeva Systems Inc.
Final Call for Papers
The 1st Workshop on Counter Speech for Online Abuse:
A workshop for creating, investigating and improving tools for producing and evaluating counter speech.
Hate speech and abusive and toxic language are prevalent in online spaces. For example, a 2019 survey shows that in the UK 30-40% of people have experienced online abuse, and platforms like Facebook bring down millions of harmful posts every year, with the help of AI tools. While removal of such content can immediately reduce the quantity of harmful messages, it can bring about accusations of censorship and may not be effective at curbing hate in the long term. An alternative approach is to reply with counter speech, i.e. targeted responses aimed at refuting the hateful language using thoughtful and cogent reasons, and fact-bound arguments. This has been shown to be effective in influencing the behaviour of both the perpetrators of abuse and bystanders that witness the interactions, as well as providing support to victims.
The sheer amount of social media data shared online on a daily basis means that hate mitigation, using counter speech, requires reliable, efficient and scalable tools. Recently, efforts have been made to curate hate countering datasets and automate the production of counter speech. However, this research field is still in its infancy, and many questions remain open regarding the most effective approaches and methods to take, as well as how to evaluate them.
This first multidisciplinary workshop aims to bring together researchers from diverse backgrounds such as computer science and the social sciences, as well as policy makers and other stakeholders to attempt to understand how counter speech is currently used to tackle abuse by individuals, activists and organisations, how Natural Language Processing (NLP) and Generation (NLG) can be applied to produce counter narratives, and the implications of using large language models for this task. It will also address, but not be limited to, the questions of how to evaluate and measure the impacts of counter speech, the importance of expert knowledge from civil society in the development of counter speech datasets and taxonomies, and how to ensure fairness and mitigate the biases present in language models when generating counter speech.
Topics
We invite papers (long and short) on a wide range of topics, including but not limited to:
• Models and methods for generating counter speech;
• Dialogue agents employing counter speech to address hateful inputs, directed towards other people or the AI itself;
• Human and automatic evaluation methods of counter speech tools;
• Multidisciplinary studies including different perspectives on the topic such as from computer science, social science, NGOs and stakeholders;
• Development of datasets and taxonomy for counter speech;
• Potentials and limitations (e.g., fairness, biases) of using large language models for generating counter speech;
• Social impact and empirical studies of counter speech on social media, including investigating the effectiveness and consequences on users of employing counter speech to fight online hate;
• Proposals for future research on counter speech, and/or preliminary results of studies in this field
We accept three types of submissions:
* Regular research papers – long (8 pages) or short (4 pages);
* Non-archival submissions: like research papers, but will not be included in the proceedings;
* Research communications: 2-4 page abstracts summarising relevant research published elsewhere.
Submission link: https://softconf.com/n/cs4oa2023
Location: co-located with SIGdialxINLG, Prague, Czechia
Important dates
All deadlines are Anywhere on Earth (UTC-12)
* Submission deadline: Jun 26, 2023
* Notification of acceptance Jul 17, 2023
* Camera-ready deadline Aug 11, 2023
* Workshop date: September 11/12 2023
Format and Styling
Submissions should follow ACL Author Guidelines<https://www.aclweb.org/adminwiki/index.php?title=ACL_Author_Guidelines> and policies for submission, review and citation, and be anonymised for double blind reviewing. Please use ACL 2023 style files; LaTeX style files and Microsoft Word templates are available at https://2023.aclweb.org/calls/style_and_formatting/<https://2021.aclweb.org/downloads/acl-ijcnlp2021-templates.zip>.
Organising Committee:
* Yi-Ling Chung, The Alan Turing Institute
* Gavin Abercrombie, Heriot-Watt University
* Helena Bonaldi, Fondazione Bruno Kessler
* Marco Guerini, Fondazione Bruno Kessler
Contact
If you have any questions, please let us know at cs4oa(a)googlegroups.com
Website: https://sites.google.com/view/cs4oa
Twitter: @cs4oa_workshop<https://twitter.com/cs4oa_workshop>
________________________________
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The contents (including any attachments) are confidential. If you are not the intended recipient of this e-mail, any disclosure, copying, distribution or use of its contents is strictly prohibited, and you should please notify the sender immediately and then delete it (including any attachments) from your system.
Dear Colleagues
We cordially invite researchers and scientists working in hyperspectral
image analysis all around the globe to participate and submit their
research work to contribute to our book titled "Computational Intelligence
based Hyperspectral Image Analysis".
It would help if you could let us know the tentative title of your
contribution within 10 days of receiving this mail so that we can plan /
structure the table of contents of the book.
Submission link: https://forms.gle/owMZQys1yd6zXtkMA
Scope of the Book:
--------------------
Computational Intelligence (CI) based hyperspectral image analysis has
gained significant importance in recent years due to its ability to extract
valuable information from hyperspectral images and make predictions.
Hyperspectral images provide a rich source of information about the
composition and properties of objects in the environment. However, the vast
amount of data generated by hyperspectral images can be overwhelming and
hard to analyze. With their ability to provide valuable insights and
improve decision-making, Computational Intelligence techniques act as a
powerful tool that aids in automatic analysis and improves accuracy. Recent
advances in the field have provided new and exciting ways to employ
CI-based hyperspectral image analysis in many diverse applications.
The book aims to showcase these latest achievements and novel approaches in
this field, focusing on their wide applications in agriculture, the
environment, defense, medical diagnostics, food and product inspection, and
mineral exploration. It will be an essential resource for those seeking to
deepen their understanding of how hyperspectral image analysis can combine
with computational intelligence techniques to solve specific tasks in
different application fields from a multidisciplinary perspective.
The topics include, but are not limited to:
---------------------------------------------
Hyperspectral Image Acquisition
Hyperspectral Image Enhancement
Hyperspectral Image Clustering
Hyperspectral Image Representation
Hyperspectral Image Restoration
Hyperspectral Image Filtering
Hyperspectral Image Classification
Hyperspectral Image Segmentation
Hyperspectral Image Retrieval and Indexing
Hyperspectral Image Compression
Spatial/Spectral Super-Resolution
Computational Imaging
Object Detection
Applications in Remote Sensing
Multispectral/Hyperspectral Image Processing: Band Selection,
Dimensionality Reduction, Compressive Sensing,
Sparse Representation, Image Registration/Matching, Image
Denoising/Destriping, Image Fusion/Pansharpening
Unsupervised Learning, Semi-supervised Learning, Transfer Learning, Deep
Learning on Hyperspectral Images
Real time Monitoring and applications
Important Dates:
---------------------
Full Chapter Submission Deadline August 30, 2023
Final Notification of Acceptance October 15, 2023
Final Chapter Submission Deadline November 15, 2023
Publisher Details:
----------------------
This book will be published in the Springer Series "Intelligent Systems
Reference Library" (Electronic ISSN: 1868-4408, Print ISSN: 1868-4394)
Indexed by: SCOPUS, SCImago, DBLP, zbMATH, Norwegian Register for
Scientific Journals and Series
Submission Guidelines:
----------------------
The length of a book chapter should be between 20 and 30 pages.
Chapters must be formatted according to Springer format (Latex or Word).
The manuscript should be submitted in Word or Latex files.
The plagiarism rate should be less than 15%.
The figure should not have any copyright issues; either it can be redrawn
or a copyright certificate should be obtained.
There is no processing or publication charge for this book.
More details on https://sites.google.com/view/cihia2023/home
-----
Best Regards
Editors:
Ajith Abraham, Flame University, Pune, India; Machine Intelligence Research
Labs (MIR Labs), USA
Anu Bajaj, Thapar Institute of Engineering and Technology, Patiala, Punjab,
India
Jyoti Maggu, Thapar Institute of Engineering and Technology, Patiala,
Punjab, India
Information contact: Anu Bajaj (er.anubajaj(a)gmail.com)
*** Last Mile for Paper Submission ***
19th IEEE eScience Conference (eScience 2023)
October 9-13, 2023, St. Raphael Resort, Limassol, Cyprus
https://www.escience-conference.org/2023/
(*** Submission Deadline Extension: June 19, 2023, AoE, FIRM!)
eScience 2023 provides an interdisciplinary forum for researchers, developers, and users of
eScience applications and enabling IT technologies. Its objective is to promote and encourage
all aspects of eScience and its associated technologies, applications, algorithms, and tools,
with a strong focus on practical solutions and open challenges. The conference welcomes
conceptualization, implementation, and experience contributions enabling and driving
innovation in data- and compute-intensive research across all disciplines, from the physical
and biological sciences to the social sciences, arts, and humanities; encompassing artificial
intelligence and machine learning methods; and targeting a broad spectrum of architectures,
including HPC, Cloud, and IoT.
The overarching theme of the eScience 2023 conference is “open eScience”. This year, the
conference is promoting four additional key topics:
• Computational Science for sustainable development
• FAIR
• Research Infrastructures for eScience
• Continuum Computing: Convergence between Cloud Computing and the Internet of Things
(IoT)
The conference is soliciting two types of contributions:
• Full papers (10 pages) presenting previously unpublished research achievements or
eScience experiences and solutions
• Posters (2 pages) showcasing early-stage results and innovations
Submitted papers should use the IEEE 8.5×11 manuscript guidelines: double-column text
using single-spaced 10-point font on 8.5×11-inch pages. Templates are available from
http://www.ieee.org/conferences_events/conferences/publishing/templates.html .
Submissions should be made via the Easy Chair system using the submission link:
https://easychair.org/conferences/?conf=escience2023 .
All submissions will be single-blind peer reviewed. Selected full papers will receive a slot for
an oral presentation. Accepted posters will be presented during a poster reception. Accepted
full papers and poster papers will be published in the conference proceedings. Rejected full
papers can be re-submitted for a poster presentation. At least one author of each accepted
paper or poster must register as an author at the full registration rate. Each author registration
can be applied to only one accepted submission.
AWARDS
eScience 2023 will host the following awards, which will be announced at the conference.
• Best Paper Award
• Best Student Paper Award
• Best Poster Award
• Best Student Poster Award
• Outstanding Early Career Contribution – this award is associated with poster submissions
and short presentations of attendees in their early career phase (i.e., postdoctoral researchers
and junior scientists).
KEY DATES
• Paper Submissions Due: June 19, 2023 (AoE) (FIRM!)
• Notification of Paper Acceptance: July 10, 2023
• Poster Submissions due: July 7, 2023 (AoE)
• Poster Acceptance Notification: July 24, 2023
• All Camera-ready Submissions due: August 14, 2023
• Author Registration Deadline: August 14, 2023
ORGANISATION
General Chair
• George Angelos Papadopoulos, University of Cyprus, Cyprus
Technical Program Co-Chairs
• Rafael Ferreira da Silva, Oak Ridge National Laboratory, USA
• Rosa Filgueira, University of St Andrews, UK
Organisation Committee
https://www.escience-conference.org/2023/organizers
Steering Committee
https://www.escience-conference.org/about/#steering-committee
Email contact: Technical-Program(a)eScience-conference.org
We are inviting applications for one fully funded PhD position (covering UK home tuition fees and stipend) in the Department of Computer Science, University of Sheffield (UK). Please forward this announcement to potentially interested candidates.
The deadline is July 10, 2023, with a starting date for the Autumn of 2023 (from September on). More details below and on this link <https://www.findaphd.com/phds/project/neural-and-cognitive-basis-of-computa…>.
About the Project: neural and cognitive basis of computational models of language
Advances in the design of computational models that learn directly from data has led to much progress in areas like natural language processing (NLP). We invite applications for a fully-funded PhD studentship on human- inspired computational models of language. This multidisciplinary project, at the intersection of machine learning, NLP and computational neuroscience, aims to develop computational models of language processing inspired by the neural and biological basis of human language.
Candidate requirements:
Applicants will need to meet general entry requirements, and ideally will have a Bachelor’s degree (or above) in Computer Science, Neuroscience, Physics, Cognitive Science, Psychology or related discipline (preferably a First Class or the equivalent from an overseas university). Experience on statistical machine learning, deep learning, or computational statistics, as well as programming experience would be desirable.
Additional English language requirements can be found here: <https://www.findaphd.com/common/clickCount.aspx?theid=153809&type=184&DID=1…>https://www.sheffield.ac.uk/postgraduate/english-language <https://www.findaphd.com/common/clickCount.aspx?theid=153809&type=184&DID=1…>.
How to apply
Applications for the PhD studentship must be made directly to the University of Sheffield using the Postgraduate Online Application Form. Make sure you name Aline Villavicencio as proposed supervisor. Information on what documents are required and a link to the application form can be found here - <https://www.findaphd.com/common/clickCount.aspx?theid=153809&type=184&DID=1…>https://www.sheffield.ac.uk/postgraduate/phd/apply/applying <https://www.findaphd.com/common/clickCount.aspx?theid=153809&type=184&DID=1…>
Funding Notes
This position is funded by a studentship from the Department of Computer Science, covering the UK home tuition fee and providing a stipend at the standard UKRI rate. International students are eligible to apply if they can self-fund the difference between the home and overseas fee.
More details on this link <https://www.findaphd.com/phds/project/neural-and-cognitive-basis-of-computa…>
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Prof. Aline Villavicencio <https://sites.google.com/view/alinev>(she/her)
Chair in Natural Language Processing
Director of Equality, Diversity and Inclusivity
Department of Computer Science, University of Sheffield
https://www.sheffield.ac.uk/dcs/people/academic/aline-villavicencio