CALL FOR PAPERS
2020 International Computer Symposium (ICS 2020), December 17-19, 2020,
Tainan, Taiwan.
http://ics2020.ncku.edu.tw/
Introduction:
International Computer Symposium (ICS) is one of the most prestigious
international ICT symposiums held in Taiwan. Founded in 1973, it is
intended to provide a forum for researchers, educators, and professionals
to exchange their discoveries and practices, and to explore future trends
and applications in computer technologies. The biennial symposium offers a
great opportunity to share research experiences and to discuss potential
new trends in the ICT industry. ICS 2020 will provide workshops, panels and
keynotes to facilitate discourse on and deepen the understanding of the
challenges in computer and communication technologies. This year, ICS 2020
will be held at Shangri-La's Far Eastern Plaza Hotel and National Cheng
Kung University, Tainan, Taiwan, on December 17-19, 2020.
The conference will include the following workshops:
1. Workshop on Algorithms and Computation Theory
2. Workshop on Artificial Intelligence on Education
3. Workshop on Artificial Intelligence Learning Theory
4. Workshop on AIoT Applications
5. Workshop on Computer Architecture, Embedded Systems, SoC, and VLSI/EDA
6. Workshop on Intelligent Network
7. Workshop on Cyber Security
8. Workshop on Big Data
9. Workshop on AR/VR and Human Computer Interaction
10. Workshop on Image Processing, Computer Graphics, and Multimedia
Technologies
11. Workshop on Web Intelligence and Social Network
12. Workshop on 5G/6G Communications, Protocols, and Applications
13. Workshop on Parallel, Distributed, and Cloud/Edge Computing
14. Workshop on Software Engineering and Programming Languages
15. Workshop on AI for Healthcare and Bioinformatics
16. Workshop on Intelligent Manufacturing
17. Workshop on Autonomous Driving
Important Dates
Paper submission due date: September 1, 2020
Paper notification: October 15, 2020
Final paper due date: November 1, 2020
Conference date: December 17-19, 2020
Papers must be submitted electronically using the IEEE Xplore compatible
PDF. All papers will be peer-reviewed. Papers should be in English,
exceeding 2 double-column pages, not exceeding 6 double-column pages
(10-point font) including figures, tables, references, and appendices.
Please use the standard IEEE conference proceedings templates for Microsoft
Word or LaTeX formats founded at
http://www.ieee.org/conferences_events/conferences/publishing/templates.html
.
Submissions should be made through the ICS 2020 submission page, handled by
the EasyChair conference management system:
https://easychair.org/conferences/?conf=ics20200
All questions about submissions should be emailed to
ics2020(a)email.ncku.edu.tw
Forwarding.
Pine
( https://meta.wikimedia.org/wiki/User:Pine )
---------- Forwarded message ---------
From: Janna Layton <jlayton(a)wikimedia.org>
Date: Fri, May 15, 2020 at 8:05 PM
Subject: [Wiki-research-l] [Wikimedia Research Showcase] May 20, 2020:
Human in the Loop Machine Learning
To: <analytics(a)lists.wikimedia.org>,
<wikimedia-l(a)lists.wikimedia.org>,
<wiki-research-l(a)lists.wikimedia.org>
Hi all,
The next Research Showcase will be live-streamed on Wednesday, May 20, at
9:30 AM PDT/16:30 UTC.
This month we will learn about recent research on machine learning systems
that rely on human supervision for their learning and optimization -- a
research area commonly referred to as Human-in-the-Loop ML. In the first
talk, Jie Yang will present a computational framework that relies on
crowdsourcing to identify influencers in Social Networks (Twitter) by
selectively obtaining labeled data. In the second talk, Estelle Smith will
discuss the role of the community in maintaining ORES, the machine learning
system that predicts the quality in Wikipedia applications.
YouTube stream: https://www.youtube.com/watch?v=8nDiu2ebdOI
As usual, you can join the conversation on IRC at #wikimedia-research. You
can also watch our past research showcases here:
https://www.mediawiki.org/wiki/Wikimedia_Research/Showcase
This month's presentations:
*OpenCrowd: A Human-AI Collaborative Approach for Finding Social
Influencers via Open-Ended Answers Aggregation*
By: Jie Yang, Amazon (current), Delft University of Technology (starting
soon)
Finding social influencers is a fundamental task in many online
applications ranging from brand marketing to opinion mining. Existing
methods heavily rely on the availability of expert labels, whose collection
is usually a laborious process even for domain experts. Using open-ended
questions, crowdsourcing provides a cost-effective way to find a large
number of social influencers in a short time. Individual crowd workers,
however, only possess fragmented knowledge that is often of low quality. To
tackle those issues, we present OpenCrowd, a unified Bayesian framework
that seamlessly incorporates machine learning and crowdsourcing for
effectively finding social influencers. To infer a set of influencers,
OpenCrowd bootstraps the learning process using a small number of expert
labels and then jointly learns a feature-based answer quality model and the
reliability of the workers. Model parameters and worker reliability are
updated iteratively, allowing their learning processes to benefit from each
other until an agreement on the quality of the answers is reached. We
derive a principled optimization algorithm based on variational inference
with efficient updating rules for learning OpenCrowd parameters.
Experimental results on finding social influencers in different domains
show that our approach substantially improves the state of the art by 11.5%
AUC. Moreover, we empirically show that our approach is particularly useful
in finding micro-influencers, who are very directly engaged with smaller
audiences.
Paper: https://dl.acm.org/doi/fullHtml/10.1145/3366423.3380254
*Keeping Community in the Machine-Learning Loop*
By: C. Estelle Smith, MS, PhD Candidate, GroupLens Research Lab at the
University of Minnesota
On Wikipedia, sophisticated algorithmic tools are used to assess the
quality of edits and take corrective actions. However, algorithms can fail
to solve the problems they were designed for if they conflict with the
values of communities who use them. In this study, we take a
Value-Sensitive Algorithm Design approach to understanding a
community-created and -maintained machine learning-based algorithm called
the Objective Revision Evaluation System (ORES)—a quality prediction system
used in numerous Wikipedia applications and contexts. Five major values
converged across stakeholder groups that ORES (and its dependent
applications) should: (1) reduce the effort of community maintenance, (2)
maintain human judgement as the final authority, (3) support differing
peoples’ differing workflows, (4) encourage positive engagement with
diverse editor groups, and (5) establish trustworthiness of people and
algorithms within the community. We reveal tensions between these values
and discuss implications for future research to improve algorithms like
ORES.
Paper:
https://commons.wikimedia.org/wiki/File:Keeping_Community_in_the_Loop-_Unde…
--
Janna Layton (she, her)
Administrative Assistant - Product & Technology
Wikimedia Foundation <https://wikimediafoundation.org/>
_______________________________________________
Wiki-research-l mailing list
Wiki-research-l(a)lists.wikimedia.org
https://lists.wikimedia.org/mailman/listinfo/wiki-research-l
International Conference on Machine learning and Cloud Computing (MLCL 2020)
June 20~21, 2020, Dubai, UAE
https://csita2020.org/mlcl/index.html
Scope
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
* Consolidation
* 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
* Platforms
* 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
Paper Submission
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).
Important Dates
* 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)
mlclconf(a)yahoo.com
## Call for Papers: NLP COVID-19 Workshop @ ACL2020
https://www.nlpcovid19workshop.org
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.
## Overview
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:
https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge
- The National Library of Medicine (US NIH) LitCovid collection:
https://www.ncbi.nlm.nih.gov/research/coronavirus/
- COVID-19 Twitter data sets:
http://www.panacealab.org/covid19/ and
https://github.com/echen102/COVID-19-TweetIDs
- COVID-19 Data Resources:
http://covid19dataresources.org/
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.
https://openreview.net/group?id=aclweb.org/ACL/2020/Workshop/NLP-COVID
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
International Conference on Artificial Intelligence and Big Data (AIBD
2020)
April 25~26, 2020, Copenhagen, Denmark
https://acsty2020.org/aibd/index.html
Scope
International Conference on Artificial Intelligence and Big Data (AIBD
2020) will provide an excellent international forum for sharing knowledge
and results in theory, methodology and applications of on Artificial
Intelligence and Big Data.
Call for Papers
International Conference on Artificial Intelligence and Big Data (AIBD
2020) will provide an excellent international forum for sharing knowledge
and results in theory, methodology and applications of on Artificial
Intelligence and Big Data. 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.
Authors are solicited to contribute to the conference by submitting
articles that illustrate research results, projects, surveying works and
industrial experiences that describe significant advances in the areas of
Artificial Intelligence and Big Data.
Topics of interest include, but are not limited to, the following
Artificial Intelligence
* AI Algorithms
* Artificial Intelligence Tools and Application
* Computational Theories of Learning
* Data Mining and Machine Learning Tools
* Fuzzy Logic
* Heuristic and AI Planning Strategies and Tools
* Hybrid Intelligent System
* Intelligent System Architecture
* Knowledge Representation
* Natural Language Processing
* Knowledge-based Systems
* Neural Networks
* Pattern Recognition
* Reasoning and Evolution
* Recent Trends and Developments of AI, Big Data
* Robotics
Big Data
* Big Data Techniques, models and algorithms
* Big Data Infrastructure and platform
* Big Data Search and Mining
* Big Data Security, Privacy and Trust
* Big Data Applications in AI, Bioinformatics, Multimedia etc
* Big Data Tools and systems
* Big Data Mining and AI
* Big Data Management
* Cloud and grid computing for Big Data
* Machine Learning and AI for Big Data
* Big Data Analytics and Social Media
* 5G and Networks for Big Data
Paper Submission
Authors are invited to submit papers through the conference Submission
System by March 21,2020. 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).
Selected papers from AIBD 2020, after further revisions, will be published
in the special issue of the following journal.
* Machine Learning and Applications: An International Journal (MLAIJ)
* International Journal of Artificial Intelligence & Applications (IJAIA)
Important Dates
Second Batch : (Submissions after February 29, 2020)
* Submission Deadline : March 21,2020
* Paper Status Notification : April 10,2020
* Final Manuscript Due : April 15,2020
Contact Us
Here's where you can reach us : aibd(a)acsty2020.org or aibdconf(a)yahoo.com
Thanks, Janna . I'm forwarding this to a few more lists.
Pine
( https://meta.wikimedia.org/wiki/User:Pine )
---------- Forwarded message ---------
From: Janna Layton <jlayton(a)wikimedia.org>
Date: Thu, Mar 12, 2020 at 7:30 PM
Subject: [Analytics] [Wikimedia Research Showcase] March 18, 2020:
Topic Modeling
To: <wiki-research-l(a)lists.wikimedia.org>,
<analytics(a)lists.wikimedia.org>, <wikimedia-l(a)lists.wikimedia.org>
Hi all,
The next Research Showcase will be live-streamed on Wednesday, March
18, at 9:30 AM PDT/16:30 UTC. We’ll have a presentation on topic
modeling by Jordan Boyd-Graber. A question-and-answer session will
follow.
YouTube stream: https://www.youtube.com/watch?v=fiD9QTHNVVM
As usual, you can join the conversation on IRC at #wikimedia-research.
You can also watch our past research showcases here:
https://www.mediawiki.org/wiki/Wikimedia_Research/Showcase
This month's presentation:
Big Data Analysis with Topic Models: Evaluation, Interaction, and
Multilingual Extensions
By: Jordan Boyd-Graber, University of Maryland
A common information need is to understand large, unstructured
datasets: millions of e-mails during e-discovery, a decade worth of
science correspondence, or a day's tweets. In the last decade, topic
models have become a common tool for navigating such datasets even
across languages. This talk investigates the foundational research
that allows successful tools for these data exploration tasks: how to
know when you have an effective model of the dataset; how to correct
bad models; how to measure topic model effectiveness; and how to
detect framing and spin using these techniques. After introducing
topic models, I argue why traditional measures of topic model
quality---borrowed from machine learning---are inconsistent with how
topic models are actually used. In response, I describe interactive
topic modeling, a technique that enables users to impart their
insights and preferences to models in a principled, interactive way. I
will then address measuring topic model effectiveness in real-world
tasks.
Overview of topic models:
https://mimno.infosci.cornell.edu/papers/2017_fntir_tm_applications.pdf
Topic model evaluation: http://umiacs.umd.edu/~jbg//docs/nips2009-rtl.pdf
Interactive topic modeling: http://umiacs.umd.edu/~jbg//docs/2014_mlj_itm.pdf
Topic Models for Categorization:
http://users.umiacs.umd.edu/~jbg//docs/2016_acl_doclabel.pdf
--
Janna Layton (she, her)
Administrative Assistant - Product & Technology
Wikimedia Foundation
_______________________________________________
Analytics mailing list
Analytics(a)lists.wikimedia.org
https://lists.wikimedia.org/mailman/listinfo/analytics
AI Mailing List,
I would like to share a hyperlink to an article about dialogue systems and crowdsourced real-time fact-checking resources: https://www.linkedin.com/pulse/dialogue-systems-crowdsourced-real-time-fact… .
The article broaches some topics on the interoperation of dialogue systems and crowdsourced resources in the context of providing accurate information to users in response to their questions with respect to topics occurring in the news or political debates.
Best regards,
Adam Sobieski
http://www.phoster.com/contents/
[You can safely skip this message if you have already seen it in the
Wikidata mailing list, and pardon for the spam]
Hi everyone,
---------------------------------------------------------------
TL;DR: soweego 2 is on its way.
Here's the Project Grant proposal:
https://meta.wikimedia.org/wiki/Grants:Project/Hjfocs/soweego_2
---------------------------------------------------------------
Does the name *soweego* ring you a bell?
It's an artificial intelligence that links Wikidata to large catalogs [1].
It's a close friend of Mix'n'match [2], which mainly caters for small
catalogs.
The next big step is to check Wikidata content against third-party
trusted sources.
In a nutshell, we want to enable feedback loops between Wikidatans and
catalog maintainers.
The ultimate goal is to foster mutual benefits in the open knowledge
landscape.
I'd be really grateful if you could have a look at the proposal page [3].
Can't wait for your feedback.
Best,
Marco
[1] https://soweego.readthedocs.io/
[2] https://tools.wmflabs.org/mix-n-match/
[3] https://meta.wikimedia.org/wiki/Grants:Project/Hjfocs/soweego_2