International Conference on Machine learning and Cloud Computing (MLCL 2020)
June 20~21, 2020, Dubai, UAE
International Conference on Machine learning and Cloud Computing (MLCL
2020) will provide an excellent international forum for sharing knowledge
and results in theory, methodology and applications of on Machine Learning
& Cloud computing. The aim of the conference is to provide a platform to
the researchers and practitioners from both academia as well as industry to
meet and share cutting-edge development in the field.
This conference aims to bring together researchers and practitioners in all
aspects of machine learning and cloud-centric and outsourced computing,
including (but not limited to):
Topics of interest include, but are not limited to, the following
* Case Studies and Theories in Cloud Computing
* Cloud Application, Infrastructure and Platforms
* Cloud Applications in Vertical Industries
* Cloud Based, Parallel Processing
* Cloud Business
* Cloud Computing Architecture
* Cloud Storage and File Systems
* Data storage and Management in Cloud Computing
* Design Tool for Cloud Computing
* Energy Management and Programming Environments
* Location Based Services, Presence, Availability, and Locality
* Machine Learning Applications
* Machine Learning in knowledge-intensive systems
* Machine Learning Methods and analysis
* Machine Learning Problems
* Machine Learning Trends
* Maintenance and Management of Cloud Computing
* Mobile Clouds for New Millennium, Mobile Devices
* Networks within Cloud systems, the Storage Area, and to the Outside
Virtualization in the Context of Cloud Computing
* NoSQL Data Stores
* Performance, SLA Management and Enforcement
* Resource Provisioning
* Security Techniques for the Cloud
* Service-Oriented Architecture in Cloud Computing
* Social Clouds (Social Networks in the Cloud)
* System Integration, Virtual Compute Clusters
* The Open Cloud: Cloud Computing and Open Source
* Virtualization on Platforms in the Cloud
Authors are invited to submit papers through the conference Submission
System by May 03, 2020. Submissions must be original and should not have
been published previously or be under consideration for publication while
being evaluated for this conference. The proceedings of the conference will
be published by Computer Science Conference Proceedings in Computer Science
& Information Technology (CS & IT) series (Confirmed).
* Submission Deadline :May 03, 2020
* Authors Notification : May 20, 2020
* Registration & camera -- Ready Paper Due :May 28, 2020
Contact us :
Here's where you can reach us : mlcl(a)csita2020.org (or)
## Call for Papers: NLP COVID-19 Workshop @ ACL2020
Paper submission deadline; June 30, 2020
Lives all around the world have been dramatically impacted by the coronavirus (COVID-19) pandemic. The global research community has mobilized to respond with timely research and scientific analysis that can contribute to our understanding and management of the virus. This workshop will specifically focus on the use of natural language processing (NLP) to address COVID-19 and/or its collateral impacts.
This workshop will host late-breaking research papers. In order to support a **rapid review process**, we will offer rolling submissions and publications using the OpenReview platform (https://openreview.net/group?id=aclweb.org/ACL/2020/Workshop/NLP-COVID).
We aim to review publications within one week and will make papers immediately available upon acceptance.
The ACL community can play a unique role in supporting research to combat COVID-19. Many valuable insights and information may be contained in vast quantities of text and speech data. Thousands of previously published research articles (and those being published on a daily basis) on coronavirus may shape our understanding of the latest virus (SARS-CoV-2) or support best practice clinical management of the disease. Analysis of millions of social media posts can help us understand how the public at large is responding to the outbreak. Identifying spreading misinformation can be critical to public health messaging. Automatic identification and organization of helpful information collected from the web can aid the public response.
There are already several research activities that are leveraging natural language processing to contribute to the study of COVID-19. For example, for the CORD-19 dataset, SketchEngine has tokenized, POS-tagged, and lemmatized the text (https://www.sketchengine.eu/covid19/), the PubAnnotation team is collecting annotations (http://pubannotation.org/collections/CORD-19), and OHSU is soliciting queries for retrieval topics (https://dmice.ohsu.edu/hersh/COVIDSearch.html).
Additionally, several publicly available corpora have emerged to support COVID-19 research:
- The Kaggle CORD-19 challenge including 40k research papers (and growing) on COVID-19 or related viruses:
- The National Library of Medicine (US NIH) LitCovid collection:
- COVID-19 Twitter data sets:
- COVID-19 Data Resources:
This ACL 2020 workshop brings together NLP researchers to discuss best practices and approaches moving forward. We welcome submissions related to any aspect of NLP applied to combat the COVID-19 pandemic, including (but not limited to):
- Text mining of scientific literature related to COVID-19 (e.g. CORD-19)
- Analysis of text from the web, social media or clinical data in support of public health activities related to COVID-19
- Sentiment analysis, mental health, or well-being analysis in social media or clinical data related to COVID-19
- Application of NLP to analysis of the collateral effects of COVID-19. Collateral effects include anything that is happening as a result of the virus, including economic effects.
- Multi-lingual or cross-lingual analysis of COVID-19 related textual data
- NLP for semantic search of COVID-19 related textual data
- Chatbots and other interactive support systems related to COVID-19
- Analysis of spoken language related to COVID-19
## Submissions and Timelines
This workshop will offer rolling submissions and publications. Publications will be reviewed within one week and made
available upon acceptance through OpenReview.
Due to the rapid review process we adopt, we will utilize **single blind** reviewing, meaning that author information will be available to the reviewers but reviewers will remain anonymous. We also adopt **open reviews** such that reviewer comments, while anonymous, will be publicly viewable. We also invite anyone to comment on the work.
- Submission deadline (long & short papers): June 30, 2020
- Main conference dates: July 5-10, 2020
- ACL 2020 Workshops: July 09-10, 2020
We expect that most of the submissions to this workshop will be short papers, given the late-breaking nature of this research.
Following ACL, **full papers** should not exceed eight (8) pages of text, plus unlimited references. Final versions of full papers will be given one additional page of content (up to 9 pages) so that reviewers' comments can be taken into account. Full papers are intended to be reports of original research.
**Short papers** may consist of up to four (4) pages of content, plus unlimited references. Appropriate short paper topics include preliminary results, application notes, descriptions of work in progress, etc.
Templates and styles files for papers are available from the ACL2020 website: http://acl2020.org/downloads/acl2020-templates.zip
An Overleaf template is also available: (https://www.overleaf.com/latex/templates/acl-2020-proceedings-template/zsrk…)
Authors should submit their papers using Open review: https://openreview.net/group?id=aclweb.org/ACL/2020/Workshop/NLP-COVID
Formal publication via the ACL Anthology will proceed after the workshop takes place.
## Organizing Committee
- Mark Dredze
- Emilio Ferrara
- Raina MacIntyre
- Jonathan May
- Robert Munro
- Cecile Paris
- Karin Verspoor
- Byron Wallace
## ACL 2020 Workshop Chairs
- Milica Gasic
- Veselin Stoyanov
- Dilek Hakkani-Tür
## ACL 2020 General Chair
- Dan Jurafsky
Given the rapidly evolving nature of this topic, we encourage contacting us with ideas and suggestions.
Please contact Karin Verspoor, karin.verspoor(a)unimelb.edu.au