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Call for Shared Task Participation: Collecting and Geocoding Armed Clash Events in Russian Ukrainian Conflict at CASE @ RANLP 2023
by ali hürriyetoglu 07 Jun '23

07 Jun '23

CASE-2023 Shared Task - Task 2: Collecting and Geocoding Armed Clash Events in Russo-Ukrainian Conflict ================================================ The unprecedented quantity of easily accessible data on social, political, and economic processes offers ground-breaking potential in guiding data-driven analysis of socio political phenomena: Armed conflicts, political movements, fights for economic and social rights, and various related socio-political happenings are reported in news articles and social media posts and recorded in curated databases. On the other hand, automatic event detection from texts and event geocoding has long been a challenge for the natural language processing (NLP) community. It requires sophisticated methods and resources, such as Machine Learning models, linguistic rules and dictionaries, geographic gazetteers. Task definition The task Collecting and Geocoding Armed Clash Events in Russo-Ukrainian Conflict is being held as a sub-task of the 6th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2023). The task will use data from the Russo-Ukrainian Conflict to test the capabilities of event detection systems to extract, geocode and de-duplicate armed clashes in news and social media postsл Evaluation will be based on the correlation between the spatio-temporal distribution and number of the extracted events and those which are in the ground truth data set. We invite contributions from researchers in NLP, ML, Deep Learning, and AI. The call is directed also towards socio-political scientists, researchers in conflict analysis and forecasting, peace studies, and computational social science. All participating teams will be able to publish their system description paper in the workshop proceedings published by ACL. For more information on the workshop, please visit the Workshop website https://emw.ku.edu.tr/case-2023/ <https://emw.ku.edu.tr/case-2022/> and the conference website https://ranlp.org/ranlp2023/. ================================================ 1. Data Gold Standard and Text Input Data for the participant systems for the time range 24.02.2022-24.08.2022 has been prepared and will be shared with the applicants on the Task website. 1.1 Training Data No training data are provided for this Task. The data utilized for CASE 2023 Task 1, which is described in Hürriyetoğlu, A. et al. (2022, 2020b), can be used for training systems for this task (Task 2). Additionally data can be used to build systems/models that can detect protest events in tweets and news articles. 1.2 Input Data The participant systems will be evaluated on raw data collections including Telegram messages, the New York Times and Ukrainian-Russian official news channels. Namely, the data collections comprise: • English language social media massage and news corpus comprising. 48.007 Telegram Messages and The New York Times News about Ukraine. • Ukrainian language social media collection comprising 102.135 Telegram Messages and Ukraine News Agency News. • Russian language social media collection comprising 8.534 Telegram Message and Russian News Agency News Further details on the text collections and sampling methods are provided in the folders news and Social Media of the github repo for the Task ( https://github.com/zavavan/case2023_task2). 1.3 Gold Standard Data The Russo-Ukrainian Conflict ground truth data primarily consists of data coming from the Armed Conflict Location & Event Data Project (ACLED). We will be adding alternative ground-truth datasets in order to prevent the bias that may be introduced by using a single definition and interpretation of an event. Full details on the manually curated data used as Gold Standard for the correlation analysis will be disclosed at the end of the evaluation period. Please check documentation on the folder gold_standard of the Task github repo. ================================================ 1. Evaluation The systems which participate in this shared task will be required to detect news articles and Telegram posts which contain description of ongoing armed clashes. The time and place of each armed clash should be detected at date level (regarding the time) and precise geographic coordinates (latitude and longitude). The systems should ideally extract event times, based on multiple text reports. In order to evaluate the ability of automatic event-coders to reproduce the gold standard armed clash event dataset, we adapt two correlation methods originally used in micro-level analysis of political violence by Hammond and Weidmann (2014), based on aggregation of event counts uniform grid geographical cells and 1-day time spans and apply a number of standard correlation coefficients and error measures. For each of the input text corpora in1.2, each participant may submit up to 3 different system responses. Each system response will consist of a csv file with the following naming pattern: “submission.<team-name>.<corpus>.<response-number>.csv” where <corpus> is either “social_media” or “news”. For instance: “submission.MyTeam.news.3.csv” for the 3rd submission of team “MyTeam” on the news corpus. Each system response file will have one line per event, where each line will have the following format: <id>,<City>,<Region>,<Country>,<Date> where <id> is a numerical event identifier, <City>,<Region>,<Country> are canonical English names of the City,State/Region and Country, respectively, of the detected event location. While only the <country> attribute is mandatory, systems are expected to assign a description of the event location at the finest grained level possible, as otherwise geographical coordinate conversion may penalize the correlation score on geographical cell aggregation. <Date> is the assigned date of the event in the format YYYY-MM-DD. A sample system response file line: 0,Kharkiv,Kharkiv Oblast,Ukraine,2022-05-02 A sample system output file can be downloaded from the Task repo at: https://github.com/zavavan/case2023_task2/blob/main/submission.myteam.news.… Important Dates (AoE time) ================================================ It is optional to use Task 1 systems. Participants may also use their own systems, which are developed independently of Task 1. Task 1 Training data available: May 1, 2023 Task 1 Test data available: May 15, 2023 Task 1 Evaluation period ends: June 30, 2023 Task 2 Sample Text archive is available: May 22, 2023 Task 2 Text archive for evaluation is available: July 1, 2023 Task 2 Evaluation period starts: July 1, 2023 Task 2 Evaluation period ends: July 24 System Description Paper submissions due: July 31, 2023 Notification to authors after review: August 7, 2023 Camera ready: August 25, 2023 Workshop period @ RANLP: Sep 7-8, 2023 Organization ================================================ - Hristo Tanev (Joint Research Centre (JRC), European Commission, Italy) - Onur Uca, Sociology (Sociology, Mersin University, Turkey) - Vanni Zavarella (University of Cagliari, Italy) - Ali Hürriyetoğlu (KNAW Humanities Cluster DHLab, the Netherlands) Please contact the organizers at hristo.tanev(a)ec.europa.eu or onuruca(a)mersin.edu.tr for your questions. 5.References Jesse Hammond and Nils B Weidmann. Using machine-coded event data for the micro-level study of political violence. Research & Politics, 1(2):2053168014539924, 2014. Hürriyetoğlu, A., Mutlu, O., Duruşan, F., Uca, O,. Gürel, A.,S., Radford, B., Dai, Y., Hettiarachchi, H., Stoehr, N., Nomoto, T., Slavcheva, M., Vargas, F., Javid, A., Beyhan, F., Yörük, E. (2022). Extended Multilingual Protest News Detection Shared Task1,CASE2021 and 2022. arXiv preprint arXiv:2211.11360. Url: https://arxiv.org/abs/2211.11360 Hürriyetoğlu, A., Yörük, E., Yüret, D., Mutlu, O., Yoltar, Ç., Duruşan, F., & Gürel, B. (2020b). Cross-context news corpus for protest events related knowledge base construction. arXiv preprint arXiv:2008.00351. In Automated Knowledge Base Construction (AKBC). URL: https://www.akbc.ws/2020/papers/7NZkNhLCjp

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Fwd: Call for Shared Task Participation: Event Causality Identification with Causal News Corpus at CASE @ RANLP 2023
by ali hürriyetoglu 07 Jun '23

07 Jun '23

*Competition Website: * https://codalab.lisn.upsaclay.fr/competitions/11784 *FIRST CALL FOR PARTICIPATION* *CASE-2023 Shared Task: Event Causality Identification with Causal News Corpus* *================================================* We invite you to participate in the CASE-2023 Shared Task: Event Causality Identification with Causal News Corpus. The task is being held as part of the 6th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2023). All participating teams will be able to publish their system description paper in the workshop proceedings published by ACL. Workshop Website: https://emw.ku.edu.tr/case-2023/ <https://emw.ku.edu.tr/case-2022/> *Motivation* *================================================* Causality is a core cognitive concept and appears in many natural language processing (NLP) works that aim to tackle inference and understanding. We are interested to study event causality in news, and therefore, introduce the Causal News Corpus. The Causal News Corpus consists of 3,767 event sentences, extracted from protest event news, that have been annotated with sequence labels on whether it contains causal relations or not. Subsequently, causal sentences are also annotated with Cause, Effect and Signal spans. Our subtasks work on the Causal News Corpus, and we hope that accurate, automated solutions may be proposed for the detection and extraction of causal events in news. *Task Overview* *================================================* We focused on two subtasks relevant to Event Causality Identification: - *Subtask 1: Causal Event Classification – *Does an event sentence contain any cause-effect meaning? - *Subtask 2: Cause-Effect-Signal Span Detection – *Which consecutive spans correspond to cause, effect or signal per causal sentence? - *Subtask 2.1: Cause-Effect Span Detection –* This subtask identifies the spans corresponding to cause and effect per sentence. - *Subtask 2.2: Signal Span Detection –* This subtask identifies the spans corresponding to the signal, or causal connective, per cause and effect relation. Participants may design solutions that work on a single, multiple or all subtasks concurrently. Participants are also allowed to combine Subtask 1 and 2 annotations for either task. However, the target labels of development and test sets should not be introduced during training in their set up in any way (E.g. even for data augmentation). This is the second iteration of this shared task. The leaderboard from last year is available at https://codalab.lisn.upsaclay.fr/competitions/2299. There are changes for both Subtask 1 and 2 data: - Added more data. Also revised annotations from previous launch - Changed traditional P, R, F1 calculations to use FairEval calculations instead *Data Content* *================================================* Our work extends a prior socio-political news corpus to annotate if event-containing sentences have causal relations or not. Our data sizes and splits are described as follows: - *Subtask 1: Causal Event Classification --* 869 news documents and 3,767 English sentences were annotated with labels on whether it contains causal relations or not. The current data splits are: 3,075 training, 340 development, 352 test. - *Subtask 2: Cause-Effect-Signal Span Detection – *Positive causal sentences from Subtask 1 were retained and annotated with Cause-Effect-Signal spans. We annotated 1,982 sentences with 2,754 causal relations. There can be multiple relations per sentence. The data splits for causal relations are: 2,257 training, 249 development, 248 test. Task Repository:* https://github.com/tanfiona/CausalNewsCorpus <https://github.com/tanfiona/CausalNewsCorpus>* Codalab Site: https://codalab.lisn.upsaclay.fr/competitions/11784 *Important Dates* *================================================* Training & Validation data available: May 01, 2023 Test data available: Jun 15, 2023 Test start: Jun 15, 2023 Test end: Jun 30, 2023 System Description Paper submissions due: Jul 10, 2023 Notification to authors after review: Aug 05, 2023 Camera ready: Aug 25, 2023 Workshop period @ RANLP: Sep 7-8, 2023 *Organization* *================================================* - Fiona Anting Tan, Institute of Data Science/ National University of Singapore, Singapore, tan.f(a)u.nus.edu - Ali Hürriyetoğlu, Koc University, Turkey, ahurriyetoglu(a)ku.edu.tr - Tommaso Caselli, Rijksuniversiteit Groningen, Netherlands, t.caselli(a)rug.nl - Nelleke Oostdijk, Radboud University, nelleke.oostdijk(a)ru.nl - Tadashi Nomoto, National Institute of Japanese Literature, Japan, nomoto(a)acm.org - Onur Uca, Mersin University, onuruca(a)mersin.edu.tr - Iqra Ameer, Centro de Investigación en Computación/ Instituto Politécnico Nacional, Mexico, iqra(a)nlp.cic.ipn.mx - Hansi Hettiarachchi, Birmingham City University, United Kingdom, hansi.hettiarachchi(a)mail.bcu.ac.uk - Farhana Ferdousi Liza, University of East Anglia, United Kingdom, f.liza(a)uea.ac.uk - Tiancheng Hu, ETH Zürich, Switzerland, tianhu(a)ethz.ch Please contact the organizer at tan.f(a)u.nus.edu with your title starting with “CNC ST”, or post questions at the Forum page in Codalab. *** You are receiving this email because you took part in this competition last year. ***

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Call for Workshop Papers and Shared Task Participation: Automated Extraction of Socio-political Events from Text - CASE @ RANLP 2023
by ali hürriyetoglu 07 Jun '23

07 Jun '23

Call for workshop papers and Shared Task participation: the 6th workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text - CASE @ RANLP 2023 ************************************************************************************ URL: https://emw.ku.edu.tr/case-2023/ Paper submission deadline: 10 July 2023 Paper acceptance notification: 5 August 2023 Paper camera-ready: 25 August 2023 Workshop dates: 7-8 September 2023 Dates and deadlines for the shared task are below. Softconf page of the workshop: https://softconf.com/ranlp23/CASE/ ************************************************************************************ We invite contributions from researchers in computer science, NLP, ML, DL, AI, socio-political sciences, conflict analysis and forecasting, peace studies, as well as computational social science scholars involved in the collection and utilization of socio-political event data. This includes (but is not limited to) the following topics 1) Extracting events and their arguments such as time and location in and beyond a sentence or document, event coreference resolution. 2) Research in NLP technologies in relation to event detection: geocoding, temporal reasoning, argument structure detection, syntactic and semantic analysis of event structures, text classification, for event type detection, learning event-related lexica, event co-reference resolution, fake news analysis, and others with a focus on real or potential event detection applications. 3) New datasets, training data collection, and annotation for event information. 4) Event-event relations, e.g., subevents, main events, spatio-temporal relations, causal relations. 5) Event dataset evaluation in light of reliability and validity metrics. 6) Defining, populating, and facilitating event schemas and ontologies. 7) Automated tools and pipelines for event collection related tasks. 8) Lexical, syntactic, semantic, discursive, and pragmatic aspects of event manifestation. 9) Methodologies for development, evaluation, and analysis of event datasets. 10) Applications of event databases, e.g. early warning, conflict prediction, policymaking. 11) Estimating what is missing in event datasets using internal and external information. 12) Detection of new and emerging SPE types, e.g. creative protests. 13) Release of new event datasets. 14) Bias and fairness of the sources and event datasets. 15) Ethics, misinformation, privacy, and fairness concerns pertaining to event datasets. 16) Copyright issues on event dataset creation, dissemination, and sharing. 17) Cross-lingual, multilingual and multimodal aspects in event analysis. 18) Resources and approaches related to contentious politics around climate change. **** Shared tasks **** Please check the workshop page and Github repositories of the respective task for additional details. Task 1 - Multilingual protest news detection: The performance of an automated system depends on the target event type as it may be broad or potentially the event trigger(s) can be ambiguous. The context of the trigger occurrence may need to be handled as well. For instance, the ‘protest’ event type may be synonymous with ‘demonstration’ or not in a specific context. Moreover, hypothetical cases such as future protest plans may need to be excluded from the results. Finally, the relevance of a protest depends on the actors as in a contentious political event only citizen-led events are in the scope. This challenge becomes even harder in a cross-lingual and zero-shot setting in case training data are not available in new languages. We tackle the task in four steps and hope state-of-the-art approaches will yield optimal results. Contact person: Ali Hürriyetoğlu (ali.hurriyetoglu(a)gmail.com) Github: https://github.com/emerging-welfare/case-2022-multilingual-event Task 2 - Collecting and Geocoding Armed Clash Events in Russian Ukrainian Conflict: There is a mismatch between the event information collected between automated and manual approaches. We aim at identifying similarities and differences between the results of these paradigms for creating event datasets. The participants of Task 1 will be invited to run the systems they will develop to tackle Task 1 on a text archive. Participation in Task 1 is not a precondition to participate in Task 2. Contact person: Hristo Tanev (htanev(a)gmail.com) and Onur Uca ( onuruca(a)mersin.edu.tr) Github: https://github.com/zavavan/case2023_task2 Task 3 - Event causality identification: Causality is a core cognitive concept and appears in many natural language processing (NLP) works that aim to tackle inference and understanding. We are interested in studying event causality in news, and therefore, introduce the Causal News Corpus. The Causal News Corpus consists of 3,767 event sentences, extracted from protest event news, that have been annotated with sequence labels on whether it contains causal relations or not. Subsequently, causal sentences are also annotated with Cause, Effect and Signal spans. Our subtasks work on the Causal News Corpus, and we hope that accurate, automated solutions may be proposed for the detection and extraction of causal events in news. Contact person: Fiona Anting Tan (tan.f(a)u.nus.edu) Github: https://github.com/tanfiona/CausalNewsCorpus Task 4 - Multimodal Hate Speech Event Detection: Hate speech detection is one of the most important aspects of event identification during political events like invasions. In the case of hate speech detection, the event is the occurrence of hate speech, the entity is the target of the hate speech, and the relationship is the connection between the two. Since multimodal content is widely prevalent across the internet, the detection of hate speech in text-embedded images is very important. Given a text-embedded image, this task aims to automatically identify the hate speech and its targets. This task will have two subtasks. Contact person: Surendrabikram Thapa (surendrabikram(a)vt.edu) Github: https://github.com/therealthapa/case2023_task4 **** Deadlines for the Shared tasks **** ** Task 1, 3, 4: Training & Validation data available: May 1, 2023 Test data available: Jun 15, 2023 Test start: Jun 15, 2023 Test end: Jun 30, 2023 System Description Paper submissions due: Jul 10, 2023 Notification to authors after review: Aug 5, 2023 Camera ready: Aug 25, 2023 ** Task 2: Sample Text archive is available: May 22, 2023 Text archive for evaluation is available: July 1, 2023 Evaluation period starts: July 1, 2023 Evaluation period ends: July 24, 2023 System Description Paper submissions due: July 31, 2023 Notification to authors after review: August 7, 2023 Camera ready: August 25, 2023 *** Keynotes *** We will continue our tradition of inviting keynote speakers from both social and computational sciences. The social science keynote will be delivered by Erdem Yörük with the title “Using Automated Text Processing to Understand Social Movements and Human Behaviour” and the computational ones will be delivered by Ruslan Mitkov and Kiril Simov. Please see the workshop webpage (https://emw.ku.edu.tr/case-2023/) for additional details.

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Faculty Position - Translation Studies and English language
by Gemma Boleda 06 Jun '23

06 Jun '23

The Department of Translation and Language Sciences <https://www.upf.edu/en/dtcl> of Universitat Pompeu Fabra <https://www.upf.edu/> (Barcelona) is seeking to fill a tenure-track *faculty position <https://apply.interfolio.com/125314> *in the area of *Translation Studies and English Language*. Duties include research and knowledge transfer, teaching, and administrative service. Research areas associated with the position: - Technological applications and innovation for language learning and translator training, including but not limited to resources and applications for classroom use, corpora resources, writing assistants, intelligent tutoring systems, digital dictionaries and/or educational data mining; - Communicative skills for transferring research outcomes to the academic community as well as society at large. Application *deadline*: July 8 2023 More information: https://apply.interfolio.com/125314 Gemma Boleda Universitat Pompeu Fabra / ICREA https://gboleda.github.io

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CLIN33 final call for abstracts, deadline June 15th
by Jens Lemmens 06 Jun '23

06 Jun '23

Dear colleague, This is the final call for abstracts for CLIN33, which will be held in Antwerp (Belgium), September 22nd 2023. We accept abstracts describing work on any aspect of computational linguistics / natural language processing (finished or in progress). Submissions must be written in English and submitted through this web form<https://docs.google.com/forms/d/e/1FAIpQLSf912QGdJYjgsuSRqqJ6uaLqYLhVXtW-ZP…> by June 15th 2023. Notifications of acceptance will be sent out on July 15th 2023 and authors of accepted abstracts will have the opportunity to submit a full paper after the conference to CLIN Journal. Those who are interested in participating in this year's shared task can find more information on the dedicated shared task website<https://sites.google.com/view/shared-task-clin33/home>. Registrations<https://forms.uantwerpen.be/en/faclw/registration-clin33/> for the event are open and have to be completed before September 5th 2023. For more info, please visit the CLIN33 website: https://clin33.uantwerpen.be/ We are looking forward to welcoming you! The CLIN33 organizers CLiPS University of Antwerp

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Lectures on Language Technology and Society
by Christian Hardmeier 06 Jun '23

06 Jun '23

We are delighted to invite you to an afternoon of public lectures on language technology and its interaction with society. The lectures will take place in Auditorium 4 at the IT University of Copenhagen on 13 June 2023, from 14:30 to 16:45 (UTC+2). Attendance is free for anyone interested. The lectures will also be streamed on Zoom. For remote participation, please register here: https://itucph.zoom.us/meeting/register/u5Utf-Chrz0sHNYzd76DD9WjJpOU0CckVsMb Speakers and titles: - Luca Maria Aiello, IT University of Copenhagen: The Language of Coordination - Shashi Narayan, Google London: Introducing Text-blueprint: Conditional generation with question-answering plans - Anne Lauscher, University of Hamburg: Ethical conversational AI - Searching for the truth? Abstracts and further information can be found here: https://christianhardmeier.rax.ch/workshop/langtech-society-2023/ -- Christian Hardmeier, Associate Professor – https://christianhardmeier.rax.ch/ IT University of Copenhagen, Department of Computer Science

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CoCo4MT Shared Task: First Call for Participation
by Katharina Kann 06 Jun '23

06 Jun '23

CoCo4MT Shared Task: First Call for Participation We are excited to introduce a new shared task for this year’s CoCo4MT workshop! Our aim is to encourage and facilitate research on corpus construction for low-resource machine translation. Corpus creation for machine translation is typically constrained by the cost and availability of human translators. When a new dataset needs to be created for a low-resource language or a specialized domain, the annotation budget should be used efficiently and any sentences chosen for translation should be of high quality and as useful for machine translation system training as possible. In this shared task, we ask participants to come up with ways in which such examples can be identified for a target language without any existing data. Specifically, given a parallel corpus between high-resource languages, the goal is to choose a good subset of the high-resource corpus to be translated into the low-resource language, in order to obtain a good training set for a machine translation system. The shared task winner will be the team whose instances result in the best final system after training. Detailed information: https://sites.google.com/view/coco4mt/shared-task Registration: https://forms.gle/jfKSPQMKEmaaXFHy5 Important Dates - May 19 2023: Release of train, dev and test data - May 30 2023: Release of baselines - July 12, 2023: Deadline to submit results - July 20, 2023: System description papers due Organizers (listed alphabetically) - Ananya Ganesh, University of Colorado Boulder - Constantine Lignos, Brandeis University - John E. Ortega, Northeastern University - Jonne Sälevä, Brandeis University - Katharina Kann, University of Colorado Boulder - Marine Carpuat, University of Maryland - Rodolfo Zevallos, Universitat Pompeu Fabra - Shabnam Tafreshi, University of Maryland - William Chen, Carnegie Mellon University -- Dr. Katharina Kann Assistant Professor of Computer Science University of Colorado Boulder Personal page: https://kelina.github.io Group page: https://nala-cub.github.io

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CFP: ML/NLP Competition on Automatic Classification of Literary Epochs (CoLiE)
by Marina Litvak 05 Jun '23

05 Jun '23

================================================== *CFP: ML/NLP Competition on Automatic Classification of Literary Epochs (CoLiE)* To advance the field of implicit temporal information retrieval from a text, this competition aims to challenge participants to develop automatic methods to identify the literary epochs of a given text, which is considered here as an implicit temporal context of a book. The task on Automatic Classification of Literary Epochs (CoLiE) aims at automatic identification of the literary epoch of a given text from its writing style: (1) Romanticism (1798-1837), (2) Victorian Literature (1837-1901), (3) Modernism (1900-1945), (4) Postmodernism (1945-2000), and (5) our days (from 2000). The competition is held as a part of the IACT’23 <https://en.sce.ac.il/news/iact23> workshop, held on July 27, 2023, in conjunction with the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval This competition is open to anyone with a passion for information retrieval, machine learning, and natural language processing. Whether you are a seasoned expert or a newcomer to the field, we welcome you to participate and extend the boundaries of automated text analysis! Competition site: http://www.kaggle.com/competitions/colie Competition Timeline - May 28, 2023: The competition is open to participants. Training and validation sets together with their labels are available. - July 10, 2023: Test dataset available. - July 17, 2023, 23:59 UTC: Final submission deadline. - July 27, 2023: The winners are announced at the special session at the IACT'23 <https://en.sce.ac.il/news/iact23> workshop. *The organizing team* - Dr. Marina Litvak (marinal(a)ac.sce.ac.il), Software Engineering Department, Shamoon College of Engineering, Beer Sheva, 84100, Israel - Dr. Irina Rabaev (irinar(a)ac.sce.ac.il), Software Engineering Department, Shamoon College of Engineering, Beer Sheva, 84100, Israel - Prof. Ricardo Campos (ricardo.campos(a)ipt.pt), Ci2 - Smart Cities Research Center, Polytechnic Institute of Tomar INESC TEC, Porto Porto, Portugal - Prof. Alípio Mário Jorge (amjorge(a)fc.up.pt) University of Porto Porto, Portugal - Prof. Adam Jatowt (adam.jatowt(a)uibk.ac.at) University of Innsbruck, Innsbruck, Austria - Mr. Vladimir Younkin (vladiyo(a)ac.sce.ac.il), Software Engineering Department, Shamoon College of Engineering, Beer Sheva, 84100, Israel -- Best regards, Marina Litvak

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Ph.D. position in NLP at the School of Engineering (University of Edinburgh)
by Shay Cohen 05 Jun '23

05 Jun '23

On behalf of Dr. Elliot Crowley from the School of Engineering at the University of Edinburgh (queries at elliot.crowley(a)ed.ac.uk): Application link: Affordable Training of Large Language Models<https://www.eng.ed.ac.uk/studying/postgraduate/research/phd/affordable-trai…> Recent developments in large language models (LLMs) have caught the attention of the public. LLMs such as OpenAI's GPT-4 and Google's Bard are able to generate remarkably realistic, coherent text based on a user's input and have the potential to be general-purpose tools used throughout society e.g. for customer service, summarising text, answering questions, writing contracts or translating between languages. However, LLMs are prohibitively expensive to train. GPT-3 (which is significantly smaller than its successor, GPT-4) has an estimated training time of 355-GPU years and an estimated training cost of $4.6M [1]. Only large, wealthy institutions can train these models and thereby control how they are trained and who gets access to them. This is undemocratic. Very recent work provides hope however. In [2] the authors explore the promising idea of “cramming”: the training of a LLM on a single GPU in a day. In [3] the authors use synthetic data to train “small” language models that can produce consistent stories at little cost. There is a huge discrepancy in quality between these models and their expensive counterparts, however. In this PhD, the student will investigate affordable LLM training i.e. with limited compute and/or data, inspired by [2,3]. Avenues of research could include (i) generating training data that facilitates fast training e.g. through dataset distillation [4]; (ii) exploring neural architecture search to develop models that are "aware" of being resource-constrained while being trained; (iii) developing novel cost-effective training algorithms, (iv) leveraging and tuning open-source LLMs. The successful student will have opportunities for collaboration within and outside Edinburgh’s School of Engineering e.g. with colleagues in the Institute for Digital Communications<https://www.eng.ed.ac.uk/research/institutes/idcom/>, The Bayesian and Neural Systems Group<https://www.bayeswatch.com/>, and Edinburgh NLP<https://edinburghnlp.inf.ed.ac.uk/>. [1] https://lambdalabs.com/blog/demystifying-gpt-3 [2] https://arxiv.org/abs/2212.14034 [3] https://arxiv.org/abs/2305.07759 [4] https://arxiv.org/abs/1811.10959 The University of Edinburgh is a charitable body, registered in Scotland, with registration number SC005336. Is e buidheann carthannais a th’ ann an Oilthigh Dhùn Èideann, clàraichte an Alba, àireamh clàraidh SC005336.

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10th European Conference On Service-Oriented And Cloud Computing (ESOCC 2023): Final Call for Submissions
by georgeangelos60＠gmail.com 05 Jun '23

05 Jun '23

*** Final Call for Submissions *** 10th European Conference On Service-Oriented And Cloud Computing (ESOCC 2023) October 24-26, 2023, Golden Bay Beach Hotel, Larnaca, Cyprus https://cyprusconferences.org/esocc2023/ (Proceedings to be published in Springer LNCS; Journal Special Issue with Springer Computing) Submission Deadline: Abstracts by June 25, 2023; Full Papers by July 2, 2023 AIM AND SCOPE Nowadays, Service-Oriented and Cloud Computing are the primary approaches to build large-scale distributed systems and deliver software services to end users. Cloud-native software is pervading the delivery of enterprise applications, as they are composed of (micro)services that can be independently developed and deployed by exploiting multiple heterogeneous technologies. Resulting applications are polyglot service compositions that can then be shipped in serverful or serverless platforms (e.g., using virtualization technologies). These characteristics make Service-Oriented and Cloud Computing the natural answers for fulfilling the industry’s need for flexibly scalable and maintainable enterprise applications, to be delivered through state-of-the-art methodologies, like DevOps. To further support this, researchers and practitioners need to create methods, tools and techniques to support cost-effective and secure development as well as use of dependable devices, platforms, services and service-oriented applications in the Cloud, now also considering the Cloud-IoT computing continuum to exploit widespread adoption of smart connected things and the increasing growth of their computing capabilities. The European Conference on Service-Oriented and Cloud Computing (ESOCC) is the premier conference on advances in the state-of-the-art and practice of Service-Oriented Computing and Cloud Computing in Europe. ESOCC aims to facilitate the exchange between researchers and practitioners in the areas of Service-Oriented Computing and Cloud Computing, as well as to explore the new trends in those areas and foster future collaborations in Europe and beyond. TOPICS OF INTEREST ESOCC 2023 seeks original, high-quality contributions related to all aspects of Service-Oriented and Cloud computing. Specific topics of interest include, but are not limited to: • Applications for Service-Oriented and Cloud Computing, e.g., big data, commerce, energy, finance, health, scientific computing, smart cities • Blockchains for Service-Oriented and Cloud Computing • Business aspects of Service-Oriented and Cloud Computing, e.g., business models, brokerage, marketplaces, costs, pricing • Business processes, e.g., service-based workflow deployment and management • Cloud interoperability, service and Cloud standards, • Cloud-IoT computing continuum, e.g., edge computing, fog computing, mobility computing, next generation services/IoT • Cloud-native architectures and paradigms, e.g., microservices and DevOps • Cloud service models, e.g., IaaS, PaaS, SaaS, DBaaS, FaaS, etc. • Deployment, composition, and management of applications in Service-Oriented and Cloud Computing • Foundations and formal methods for Service-Oriented and Cloud Computing • Enablers for Service-Oriented and Cloud Computing, e.g., service discovery, orchestration, matchmaking, monitoring, and analytics • Model-Driven Engineering for Service-Oriented and Cloud Computing • Multi-Cloud, cross-Cloud, and federated Cloud solutions • Requirements engineering, design, development, and testing of applications in Service-Oriented and Cloud Computing • Semantic services and service mining • Service and Cloud middlewares and platforms • Software/service adaptation and evolution in Service-Oriented and Cloud Computing • Storage, computation and network Clouds • Sustainability and energy issues in Service-Oriented and Cloud Computing • Quality aspects (e.g., governance, privacy, security, and trust) of Service-Oriented and Cloud Computing • Quality of Service (QoS) and Service-Level Agreement (SLA) for Service-Oriented and Cloud Computing • Social aspects of Service-Oriented and Cloud Computing, e.g., crowdsourcing services, social and crowd-based Clouds • Virtualization for Service-Oriented and Cloud Computing, e.g., serverless, container-based virtualization, VMs IMPORTANT DATES • Submission of abstracts: June 25th, 2023 (AoE) • Submission of full papers: July 2nd, 2023 (AoE) • Notification to authors: August 4th, 2023 (AoE) • Camera-ready versions due: August 21st, 2023 (AoE) • Author registration due: August 21st, 2023 (AoE) TYPES OF SUBMISSIONS ESOCC 2023 invites submissions of the following kinds: • Regular Research Papers (15 pages including references) • PhD Symposium (12 pages including references) • Projects and Industry Reports (Projects and Industry Reports (1 to 6 pages including references, describing an ongoing EU or national project, or providing industrial perspectives on innovative applications, technologies, or methods in ESOCC’s scope) We only accept original papers, not submitted for publication elsewhere. The papers must be formatted according to the proceedings guidelines of Springer’s Lecture Notes in Computer Science (LNCS) series (http://www.springer.com/lncs). They must be submitted to the EasyChair site at:  https://easychair.org/conferences/?conf=esocc2023 by selecting the right track. Accepted papers from all tracks will be published in the main conference proceedings by Springer in the LNCS series. For publication to happen, at least one author of each accepted paper is expected to register and present the work at the conference. The best papers accepted will be invited to submit extended versions for a Journal Special Issue to be published by Springer Computing. ORGANISATION General Chair • George A. Papadopoulos, University of Cyprus, CY (george at ucy.ac.cy) Program Chairs • Florian Rademacher, University of Applied Sciences and Arts Dortmund, DE (florian.rademacher at fh-dortmund.de) • Jacopo Soldani, University of Pisa, IT (jacopo.soldani at unipi.it) Steering and Program Committee https://cyprusconferences.org/esocc2023/committees/

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