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/