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CfP Special Issue "Artificial Intelligence and Smart Technologies for Achieving Sustainable Goals"
by Hend Al-Khalifa 13 Jun '23

13 Jun '23

Dear Colleagues, We invite you to submit a paper to a special issue about "Artificial Intelligence and Smart Technologies for Achieving Sustainable Goals <https://www.mdpi.com/journal/sustainability/special_issues/Z1I87XD9C3>". Deadline for manuscript submissions: *30 August 2023* *Introduction* The United Nations’ Sustainable Development Goals (SDGs) provide a blueprint for a fairer and more sustainable world for everyone. One of the key ways to achieve these goals is through the use of artificial intelligence (AI) and smart technologies. Artificial intelligence (AI) has been hailed as a game-changer for sustainable development. It has the potential to help achieve the Sustainable Development Goals (SDGs) in a number of ways, including by providing decision support for sustainable development planning, helping to optimize resource use, and increasing transparency and accountability. Smart technologies offer great potential for sustainable development applications. Smart technologies can help to improve the efficiency of resource use, e.g., by reducing wastage, and can also help to improve transparency and accountability. In this Special Issue, we invite papers that explore the potential of AI and smart technologies for sustainable development. We are particularly interested in papers that describe applications of these technologies that have the potential to make a real difference to the achievement of SDGs. We welcome papers from all sectors, including academia, industry, government, and non-governmental organizations (NGOs). Topics of interest include, but are not limited to, the following: - Applications of AI and smart technologies in sustainable development; - The potential of AI and smart technologies to help achieve the SDGs; - Barriers and challenges to the use of AI and smart technologies for sustainable development; - Future directions for the use of AI and smart technologies in sustainable development; - Ethical considerations in the use of AI and smart technologies for sustainable development; - The role of AI and smart technologies in sustainable development policy. Submitted papers should not have been previously published nor be currently under consideration for publication elsewhere. All papers will be thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Special issue page: https://www.mdpi.com/journal/sustainability/special_issues/Z1I87XD9C3 -- Hend S. Al-Khalifa, PhD Professor Information Technology Department CCIS, King Saud University Saudi Arabia, Riyadh Website: http://fac.ksu.edu.sa/hendk Research: http://iwan.ksu.edu.sa/ Tel: +966-11-8051437 =============== I strive to make a science of my teaching, and when appropriate, I disseminate the results.

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Call for participation - Arabic NER Shared Task 2023
by nagham ghanim 13 Jun '23

13 Jun '23

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

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ELRA Catalogue of Language Resources - Update
by info＠elda.org 13 Jun '23

13 Jun '23

[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./

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Fully funded PhD Scholarship in Causal Machine Learning for Multi-modal data in NLP/Healthcare
by Mohammed Hasanuzzaman 13 Jun '23

13 Jun '23

*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/ [image: Mailtrack] <https://mailtrack.io?utm_source=gmail&utm_medium=signature&utm_campaign=sig…> Sender notified by Mailtrack <https://mailtrack.io?utm_source=gmail&utm_medium=signature&utm_campaign=sig…> 13/06/23, 13:09:55

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Deadline extension: 3rd Workshop on Computational Linguistics for the Political and Social Sciences (CPSS @ KONVENS 2023)
by Gabriella Lapesa 13 Jun '23

13 Jun '23

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)

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Job Opening for Senior Data Scientist (Remote - NLP for medical domain and life sciences)
by Ehsan Khoddam Mohammadi 13 Jun '23

13 Jun '23

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.

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Final CfP: The 1st Workshop on Counter Speech for Online Abuse
by Abercrombie, Gavin 12 Jun '23

12 Jun '23

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> ________________________________ Founded in 1821, Heriot-Watt is a leader in ideas and solutions. With campuses and students across the entire globe we span the world, delivering innovation and educational excellence in business, engineering, design and the physical, social and life sciences. This email is generated from the Heriot-Watt University Group, which includes: 1. Heriot-Watt University, a Scottish charity registered under number SC000278 2. Heriot- Watt Services Limited (Oriam), Scotland's national performance centre for sport. Heriot-Watt Services Limited is a private limited company registered is Scotland with registered number SC271030 and registered office at Research & Enterprise Services Heriot-Watt University, Riccarton, Edinburgh, EH14 4AS. 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.

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Call for Book Chapters: Computational Intelligence based Hyperspectral Image Analysis, Springer (Scopus Indexed)
by Anu Bajaj 12 Jun '23

12 Jun '23

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)

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19th IEEE eScience Conference (eScience 2023): Last Mile for Paper Submission
by georgeangelos60＠gmail.com 12 Jun '23

12 Jun '23

*** 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

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SemDial 2023 - MariLogue: Registration open!
by Andy Lücking 12 Jun '23

12 Jun '23

* Apologies for cross-posting * The registration for SemDial 2023 - MariLogue, the 27th Workshop on the Semantics and Pragmatics of Dialogue is now open! Got to: https://mezzanine.um.si/en/conference/semdial-2023-marilogue/ Note that the submission dateis close: June 16th, 2023 (Submissions website: https://easychair.org/conferences/?conf=semdial2023marilogue)

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