The 2nd International Conference on Applications of Intelligent Systems,
APPIS 2019 <http://appis.webhosting.rug.nl/2019/>, will be held on 7-12
January 2019 in Las Palmas de Gran Canaria, Spain.
APPIS 2019 is organized by the University of Groningen and the
University of Las Palmas de Gran Canaria, and includes a Winter School
on Machine Learning (WISMAL 2019)
<http://appis.webhosting.rug.nl/2019/tutorials-appis-2019/>.
APPIS 2019 welcomes submission of *abstracts* (1-2 pages) and*full
papers* (4-6 pages) related, but not limited to the following topics:
* Machine learning and representation learning
* Images, videos and time-series analysis
* Statistical and structural pattern recognition
* Data visualization and dimensionality reduction
* Robotics
* Intelligent systems in health and medicine
* Cyber computing and security
* Bio-informatics
* Data mining
* Cognitive discovery
* Algorithms for embedded and real-time systems
* Semantic technologies
* Intelligent buildings
* Intelligent sensors and sensor networks
* Augmented reality
* Adaptive systems
* Fuzzy systems
* Human-machine interaction
* Natural language processing
* Situation awareness systems
* Recommender systems
Papers must be submitted electronically, with *deadline November 7th
(23:59:59 CET)*, through the APPIS 2019 conference web site in pdf
format and must conform to the ACM template and style file. Proceedings
will be published in ACM ICPS.
Each accepted paper must be presented by one of the authors and
accompanied by at least one full registration fee payment (250 Euro), to
guarantee publication in the proceedings.
In order to be published in the conference proceedings, *abstract
submissions* need to be extended to full papers before the conference
(*deadline January 5, 2019*)
Find more information in the submission page
<http://appis.webhosting.rug.nl/2019/paper-submission/>.
We look forward to meet you in Las Palmas de Gran Canaria!
Best regards,
Nicolai Petkov
Nicola Strisciuglio
Carlos Travieso-Gonzalez
Hello All,
I have created a new labelling campaign, "Newcomer Session Quality" which
describes observes the *sessions* (temporally related edits) of users on
their *registration day* [1]. The idea is to detect potentially productive
newcomers may be struggling with onboarding and might otherwise get bitten.
I was wondering if there was a standard way to advertise for volunteers for
a new campaign on-wiki? Would it be OK to leave talk-page invitations to
users who have signed up for updates on other campaigns [2]? By the way,
right now the campaign is just in enwiki for testing, but I have 6 other
languages ready to go if enwiki labelling goes well.
[1] https://en.wikipedia.org/wiki/Wikipedia:Labels/Newcomer_session_quality
[2] https://en.wikipedia.org/wiki/Wikipedia:Labels/Edit_quality
Make a great day,
Max Klein ‽ http://notconfusing.com/
The 2nd International Conference on Applications of Intelligent Systems,
APPIS 2019 <http://appis.webhosting.rug.nl/2019/>, will be held on 7-12
January 2019 in Las Palmas de Gran Canaria, Spain.
APPIS 2019 is organized by the University of Groningen and the
University of Las Palmas de Gran Canaria, and includes a Winter School
on Machine Learning (WISMAL 2019)
<http://appis.webhosting.rug.nl/2019/tutorials-appis-2019/>.
APPIS 2019 welcomes submission of *abstracts* (1-2 pages) and*full
papers* (4-6 pages) related, but not limited to the following topics:
* Machine learning and representation learning
* Images, videos and time-series analysis
* Statistical and structural pattern recognition
* Data visualization and dimensionality reduction
* Robotics
* Intelligent systems in health and medicine
* Cyber computing and security
* Bio-informatics
* Data mining
* Cognitive discovery
* Algorithms for embedded and real-time systems
* Semantic technologies
* Intelligent buildings
* Intelligent sensors and sensor networks
* Augmented reality
* Adaptive systems
* Fuzzy systems
* Human-machine interaction
* Natural language processing
* Situation awareness systems
* Recommender systems
Papers must be submitted electronically, with *deadline October 26th*,
through the APPIS 2019 conference web site in pdf format and must
conform to the ACM template and style file. Proceedings will be
published in ACM ICPS.
Each accepted paper must be presented by one of the authors and
accompanied by at least one full registration fee payment (250 Euro), to
guarantee publication in the proceedings.
In order to be published in the conference proceedings, *abstract
submissions* need to be extended to full papers before the conference
(*deadline January 5, 2019*)
Find more information in the submission page
<http://appis.webhosting.rug.nl/2019/paper-submission/>.
We look forward to meet you in Las Palmas de Gran Canaria!
Best regards,
Nicolai Petkov
Nicola Strisciuglio
Carlos Travieso-Gonzalez
Hey folks,
I wanted to ask a question of people on this list. Up until now, I have
been rejecting postings from non-list members that were just blind Calls
for Papers (CFPs) for various journals and conferences. It seems to me
that we all get plenty of spam like this and that we don't need to also
receive it via our mailing lists.
Should I continue to block this content?
FWIW, I don't see any problem with our list member sharing CFPs for
conferences/journals that they think are important. That should be allowed
through. I've just been rejecting the drive-by non-member CFP postings.
-Aaron
There is a new paper out about "Using the Tsetlin Machine to Learn Human -
Interpretable Rules for High - Accuracy Text Categorization with Medical
Applications" [1] or in our context "…High - Accuracy Text Categorization
of unsourced statements".
Their results on text categorization is quite promising. I've been
wondering why they get so good results, and I suspects it either has to do
with implicit regularization (kind of dropouts), or some other effects I
suspect can be important when you start comparing really good results. One
is that learning binary weights uses less information entropy than learning
weights with higher quantization (more bits), thus with limited training
data more of the available information entropy goes into learning the
actual rules. Another possibility is that the learning algorithm finds
better minimums (actually maximums) than the other algorithms. Ie the
algorithm find stable solutions, that is the real minimum. A third
possibility is that the learning is faster because it does not backprop
(thus more stable and converge faster).
The generated rules are much easier to handle in the users own browser, and
instead of using a central server the text categorization (classification)
can be done in the users own browser. That will make the interaction more
responsive.
In my opinion this is a neural network. The generated rules can be
reformulated as a disjunctiove normal forms, and then it is more obvious.
There are binary weights, weight multiplication done with and-operators,
and summation done by or-operators.
There are more background in the paper "The Tsetlin Machine - A Game
Theoretic Bandit Driven Approach to Optimal Pattern Recognition with
Propositional Logic"
[1] https://arxiv.org/abs/1809.04547
[2] https://arxiv.org/abs/1804.01508
John Erling Blad
/jeblad