The Journal Information Fusion made it to rank 2 out of 136 journals in the field of Artificial Intelligence, congrats to Francisco Herrera, this is excellent for our special issue on rAI – which goes beyond xAI towards accountability, privacy, safety and security.
Welcome to our XXAI ICML 2020 workshop: extending explainable ai beyond deep models and classifiers
Inaugural Editorial Paper published:
Holzinger, A. 2017. Introduction to Machine Learning & Knowledge Extraction (MAKE). Machine Learning and Knowledge Extraction, 1, (1), 1-20, doi:10.3390/make1010001.
https://www.mdpi.com/2504-4990/1/1/1
Machine Learning and Knowledge Extraction (MAKE) is an inter-disciplinary, cross-domain, peer-reviewed, scholarly open access journal to provide a platform to support the international machine learning community. It publishes original research articles, reviews, tutorials, research ideas, short notes and Special Issues that focus on machine learning and applications. Papers which deal with fundamental research questions to help reach a level of useable computational intelligence are very welcome.
Machine learning deals with understanding intelligence to design algorithms that can learn from data, gain knowledge from experience and improve their learning behaviour over time. The challenge is to extract relevant structural and/or temporal patterns (“knowledge”) from data, which is often hidden in high dimensional spaces, thus not accessible to humans. Many application
domains, e.g., smart health, smart factory, etc. affect our daily life, e.g., recommender systems, speech recognition, autonomous driving, etc. The grand challenge is to understand the context in the real-world under uncertainty. Probabilistic inference can be of
great help here as the inverse probability allows to learn from data, to infer unknowns, and to make predictions to support decision making.
NOTE: To support the training of a new kind of machine learning graduates, the journal accepts peer-reviewed high-end tutorial papers, similar as the IEEE Signal Processing Magazine (SCI IF=9.654 !) is doing:
https://ieeexplore.ieee.org/xpl/aboutJournal.jsp?punumber=79#AimsScope
https://bmcmedinformdecismak.biomedcentral.com/articles/collections/odds
Note: Excellent submissions to the IFIP Cross Domain Conference on Machine Learning and Knowledge Discovery (CD-MAKE), (Submission due to May, 15, 2017) relevant to the topics described below, will be invited to expand their work into this thematic series:
The use of open data for discovery science has gained much attention recently as its full potential is unfolding and being explored in projects spanning all areas of healthcare research. A plethora of data sets are now available thanks to drives to make data universally accessible and usable for discovery science. However, with these advances come inherent challenges with the processing and management of ever expanding data sources. The computational and informatics tools and methods currently used in most investigational settings are often labor intensive and rely upon technologies that have not been designed to scale and support reasoning across multi-dimensional data resources. In addition, there are many challenges associated with the storage and responsible use of open data, particularly medical data, such as privacy, data protection, safety, information security and fair use of the data. There are therefore significant demands from the research community for the development of data management and analytic tools supporting heterogeneous analytic workflows and open data sources. Effective anonymisation tools are also of paramount importance to protect data security whilst preserving the usability of the data.
The purpose of this thematic series is to bring together articles reporting advances in the use of open data including the following:
Submission is open to everyone, and all submitted manuscripts will be peer-reviewed through the standard BMC Medical Informatics and Decision Making review process. Manuscripts should be formatted according to the submission guidelines and submitted via the online submission system. Please indicate clearly in the covering letter that the manuscript is to be considered for the ‘Open data for discovery science’ collection. The deadline for submissions will be 31 July 2017.
For further information, please email the editors of the thematic series:
Andreas HOLZINGER a.holzinger@human-centered.ai,
Philip PAYNE prpayne@wustl.edu ,or the BMC in-house editor
Emma COOKSON at emma.cookson@biomedcentral.com
Link to the IFIP Cross-Domain Conference on Machine Learning and Knowledge Extraction (CD-MAKE):
https://cd-make.net
https://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=61244©ownerid=17803
Call for Papers due to May, 15, 2017
International IFIP Cross Domain Conference for Machine Learning & Knowledge Extraction CD-MAKE
in Reggio di Calabria (Italy) August 29 – September 1, 2017
CD stands for Cross-Domain and means the integration and appraisal of different fields and application domains (e.g. Health, Industry 4.0, etc.) to provide an atmosphere to foster different perspectives and opinions. The conference is dedicated to offer an international platform for novel ideas and a fresh look on the methodologies to put crazy ideas into Business for the benefit of the human. Serendipity is a desired effect, and shall cross-fertilize methodologies and transfer of algorithmic developments.
MAKE stands for MAchine Learning & Knowledge Extraction.
CD-MAKE is a joint effort of IFIP TC 5, IFIP WG 8.4, IFIP WG 8.9 and IFIP WG 12.9 and is held in conjunction with the International Conference on Availability, Reliability and Security (ARES).
Keynote Speakers are Neil D. LAWRENCE (Amazon) and Marta MILO (University of Sheffield).
IFIP is the International Federation for Information Processing and the leading multi-national, non-governmental, apolitical organization in Information & Communications Technologies and Computer Sciences, is recognized by the United Nations and was established in the year 1960 under the auspices of the UNESCO as an outcome of the first World Computer Congress held in Paris in 1959.
Papers are sought from the following seven topical areas (see image below). Papers which deal with fundamental questions and theoretical aspects in machine learning are very welcome.
❶ Data science (data fusion, preprocessing, data mapping, knowledge representation),
❷ Machine learning (both automatic ML and interactive ML with the human-in-the-loop),
❸ Graphs/network science (i.e. graph-based data mining),
❹ Topological data analysis (i.e. topology data mining),
❺ Time/entropy (i.e. entropy-based data mining),
❻ Data visualization (i.e. visual analytics), and last but not least
❼ Privacy, data protection, safety and security (i.e. privacy aware machine learning).
Proposals for Workshops, Special Sessions, Tutorials: April, 19, 2017
Submission Deadline: May, 15, 2017
Author Notification: June, 14, 2017
Camera Ready Deadline: July, 07, 2017
Special Session on September, 1, 2017, organized by Andreas HOLZINGER, Peter KIESEBERG, Edgar WEIPPL and A Min TJOA in the context of the 12th International Conference on Availability, Reliability and Security (ARES and CD-ARES), Reggio di Calabria, Italy, August 29 – September, 2, 2017
supported by the International Federation of Information Processing IFIP > TC5 and WG 8.4 and WG 8.9
https://cd-ares-conference.eu
https://www.ares-conference.eu
Keynote Talk by Neil D. LAWRENCE, University of Sheffield and Amazon
With the new European data protection and privacy regulations coming into effect with January, 1, 2018 issues having been nice to have so far are becoming a must have. Privacy aware machine learning will be one of the most important fields for the European research community and the IT business in particular. Most affected is the whole area of biology, medicine and health, partiuclarly driven by the fact that health sciences are becoming a more and more data intensive science.
This special session will bring together scientists with diverse background, interested in both the underlying theoretical principles as well as the application of such methods for practical use in the biomedical, life sciences and health care domain. The cross-domain integration and appraisal of different fields will provide an atmosphere to foster different perspectives and opinions; it will offer a platform for novel crazy ideas and a fresh look on the methodologies to put these ideas into business.
All paper will be peer-reviewed by three members of the international PAML-commitee. Paper acceptance rate of the last session was 35 %. Accepted papers will be published in a Springer Lecture Notes in Computer Science (LNCS) Volume and excellent contributions will be invited to be extented in a special issue of a journal (planned Springer MACH and/or BMC MIDM).
Research topics covered by this special session include but are not limited to the following topics:
– Production of Open Data Sets
– Synthetic data sets for learning algorithm testing
– Privacy preserving machine learning, data mining and knowledge discovery
– Data leak detection
– Data citation
– Differential privacy
– Anonymization and pseudonymization
– Securing expert-in-the-loop machine learning systems
– Evaluation and benchmarking
This picture was taken by our local host, Francesco Buccafurri on January, 3, 2017: from the conference venue you have a direct view to the Aetna volcano:
Picture taken by Francesco Buccafurri on January, 3, 2017
We are organizing a special session on Privacy Aware Machine Learning for Health Data Science at the 11th international Conference on Availability, Reliability and Security (ARES and CD-ARES), Salzburg, Austria, August 29 – September, 2, 2016
supported by the International Federation of Information Processing IFIP > TC5 and WG 8.4 and WG 8.9
https://cd-ares-conference.eu
https://www.ares-conference.eu
Keynote Talk by Bernhard SCHÖLKOPF, Max Planck Institute for Intelligent Systems, Empirical Inference Department
We are proud to welcome Bernhard Schölkopf as Keynote Speaker to the ARES/CD-ARES conference in Salzburg
Machine learning is the fastest growing field in computer science [Jordan, M. I. & Mitchell, T. M. 2015. Machine learning: Trends, perspectives, and prospects. Science, 349, (6245), 255-260], and it is well accepted that health informatics is amongst the greatest challenges [LeCun, Y., Bengio, Y. & Hinton, G. 2015. Deep learning. Nature, 521, (7553), 436-444 ], e.g. large-scale aggregate analyses of anonymized data can yield valuable insights addressing public health challenges and provide new avenues for scientific discovery [Horvitz, E. & Mulligan, D. 2015. Data, privacy, and the greater good. Science, 349, (6245), 253-255]. Privacy is becoming a major concern for machine learning tasks, which often operate on personal and sensitive data. Consequently, privacy, data protection, safety, information security and fair use of data is of utmost importance for health data science.
The amount of patient-related data produced in today’s clinical setting poses many challenges with respect to collection, storage and responsible use. For example, in research and public health care analysis, data must be anonymized before transfer, for which the k-anonymity measure was introduced and successively enhanced by further criteria. As k-anonymity is an NP-hard problem, which cannot be solved by automatic machine learning (aML) approaches we must often make use of approximation and heuristics. As data security is not guranteed given a certain k-anonymity degree, additional measures have been introduced in order to refine results (l-diversity, t-closeness, delta-presence). This motivates methods, methodologies and algorithmic machine learning approaches to tackle the problem. As the resulting data set will be a tradeoff between utility, usability and individual privacy and security, we need to optimize those measures to individual (subjective) standards. Moreover, the efficacy of an algorithm strongly depends on the background knowledge of an potential attacker as well as the underlying problem domain. One possible solution is to make use of interactive machine learning (iML) approaches and put a human-in-the-loop where the central question remains open: “could human intelligence lead to general heuristics we can use to improve heuristics?”
Research topics covered by this special session include but are not limited to the following topics:
– Production of Open Data Sets
– Synthetic data sets for learning algorithm testing
– Privacy preserving machine learning, data mining and knowledge discovery
– Data leak detection
– Data citation
– Differential privacy
– Anonymization and pseudonymization
– Securing expert-in-the-loop machine learning systems
– Evaluation and benchmarking
This special session will bring together scientists with diverse background, interested in both the underlying theoretical principles as well as the application of such methods for practical use in the biomedical, life sciences and health care domain. The cross-domain integration and appraisal of different fields will provide an atmosphere to foster different perspectives and opinions; it will offer a platform for novel crazy ideas and a fresh look on the methodologies to put these ideas into business.
Accepted Papers will be published in a Springer Lecture Notes in Computer Science LNCS Volume.
Schedule:
I) Deadline for submissions: April, 30, 2016
Paper submission via:
https://cd-ares-conference.eu/?page_id=43
II) Camera Ready deadline: July, 4, 2016
III) Special Session: August, 30, 2016
> Conference Venue
> Travel Information Salzburg
> Lonely Planet Salzburg
The International Scientific Committee – consisting of experts from the international expert network HCI-KDD dealing with area (7), privacy, data protection, safety and security and additionally invited international experts will ensure the highest possible scientific quality, each paper will be reviewed by at least three reviewers (the paper acceptance rate of the last special session was 35 %).
Submission deadline: June 1, 2015
Advanced topological measures from the numerical and algebraic perspective, combined with the geometric representations of physical objects and the sparse decomposition using harmonic transforms are generating novel methods for the study of n-dimensional digital or continuous images. The mutual interdependence between harmonic analysis, geometry and topology supports the thesis that these different sources of mathematical information are necessary to fully characterize the spatially structured clouds of points at any dimension. In this special issue, the focus will be on novel methods of multi-dimensional and multi-variate image analysis and image processing using computational harmonic or geometric-topological techniques and algorithms.
The applications envisaged are in multidisciplinary engineering, paying particular attention to recent trends in the industrial setting and in any image-related topic situated at the interplay between these computational areas.
Main Topics of Interest:
Use of of harmonic analysis, topological and/or geometric information in image applications.
Computational harmonic analysis, topology or geometry applied to image processing;
Interactions between computational harmonic analysis, geometry and topology in image context;
Geometric and/or harmonic modeling guided by topological constraints;
Algorithm optimization for image applications, transfer of mathematical tools, parallel computation in image context and hierarchical approaches;
Pattern recognition from a harmonic, topological and/or geometrical viewpoint.
Combinatorial, geometric, topological, fractal or multi-resolution models.
Algebraic-topological and/or geometric invariants and features for n-dimensional images and their computation.
Submission Information:
See detailed Guide for Authors here: https://www.elsevier.com/journals/pattern-recognition-letters/0167-8655/guide-for-authors Papers can have a maximum length of 10 pages in the journal template.
Submit your paper here: https://ees.elsevier.com/patrec. Make sure to select ” SI: GeToHa” as the Article type. Submission is possible starting from May 1 2015. Submission deadline is June 1th, 2015
Papers will be reviewed according to the normal journal standards. Papers will receive at most two rounds of reviews. We will strive to finish the first round of review four to six weeks after submission.
For more information, please contact the Managing Guest Editor.
Pedro Real, Managing Guest Editor
Institute of Mathematics of Seville University (IMUS)
ETS. Ingeniería Informática, University of Seville, Spain
Darian Onchis Moaca, Guest Editor
Eftimie-Murgu University, Romania
https://homepage.univie.ac.at/darian.onchis/
Helena Molina-Abril, Guest Editor
The Maimonides Institute for Biomedical Research of Cordoba (IMIBIC), Spain
Mihail Gaianu, Guest Editor
West University of Timisoara, Romania
The 2015 International Conference on Brain Informatics and Health (BIH’15)
Informatics for Brain Science, Human Behavior and Brain Health
August 30 – September 2, 2015, London, UK
CALL FOR PAPERS
Homepage:
(FULL PAPER SUBMISSION DEADLINE: April 5, 2015)
*** KEYNOTE SPEAKERS (Confirmed) ***
Allan Jones (Allen Institute for Brain Science, USA)
Henry Markram (EPFL, Switzerland)
David Van Essen (Washington University School of Medicine, USA)
*****************
Brain research is rapidly advancing with the application of big data technology to neuroscience as can be seen in major international initiatives in the US, Europe and Asia. BIH’15 reflects that brain informatics has emerged as a distinct field and crosses the disciplines of neuroscience, cognitive science, computer science, signal processing, and neuroimaging technologies as well as data science. Following the success of past conferences in this series, BIH’15 will take place at Imperial College London, in UK, gathers the researchers from major international brain research projects, and plans an industrial exhibition.
BIH’15 draws special attention to informatics for brain science, human behavior and brain health. BIH’15 will address big data approaches to both the brain and behaviour, with a strong emphasis on emerging trends of big data analysis and management technology for BI, active media technology in behavior learning, and real-world applications for brain and mental health.
BIH’15 welcomes paper submissions (full paper and abstract submissions). Both research and application papers are solicited. All submitted papers will be reviewed on the basis of technical quality, relevance, significance and clarity. Accepted full papers will be included in the proceedings by Springer LNCS/LNAI.
Tutorial, Satellite symposium and Special-Session proposals and Industry/Demo-Track papers are also welcome.
IMPORTANT DATES:
================
Satellite symposium proposal submission: March 10, 2015
Notification of satellite symposium acceptance: March 30, 2015
Submission of full papers: April 5, 2015
Submission of abstracts: May 20, 2015
Submission of satellite symposium papers: May 20, 2015
Notification of full paper acceptance: May 25, 2015
Notification of abstract acceptance: June 10, 2015
Notification of satellite symposium paper acceptance: June 10, 2015
Tutorial proposal submission: May 15, 2015
Satellite symposiums: August 30, 2015
Main conference: August 31-September 2, 2015
PAPER SUBMISSIONS & PUBLICATIONS:
=================================
TYPE-I (Full Paper Submissions; Submission Deadline: April 5, 2015):
Papers need to have up to 10 pages in LNCS format:
https://www.springer.com/computer/lncs?SGWID=0-164-6-793341-0.
All full length papers accepted (and all special sessions’ full
length papers) will be published by Springer as a volume of
the series of LNCS/LNAI.
TYPE-II (Abstract Submissions; Submission Deadline: May 20, 2015):
Abstracts have a word limit of 500 words. Experimental research is
particularly welcome. Accepted abstract submissions will be included
in the conference program, and will be published as a single,
collective proceedings volume.
Title: Include in the title of the abstract all words critical for a
subject index. Write your title in sentence case (first letter is
capitalized; remaining letters are lower case). Do not bold or
italicize your full title.
Author: List all authors who contributed to the work discussed in the
abstract. The presenting author must be listed in the first author
slot of the list. Be prepared to submit contact information as well as
conflict of interest information for each author listed.
Abstract: Enter the body of the abstract and attach any applicable
graphic files or tables here. Do not re-enter the title, author,
support, or other information that is collected in other steps of the
submission form.
Presentation Preference: Authors may select from three presentation
formats when submitting an abstract: “poster only,”, “talk
preferred” or “no preference.” The “talk preferred” selection
indicates that you would like to give a talk, but will accept a poster
format if necessary. Marking “poster only” indicates that you would
not like to be considered for an oral-presentation session. Selecting
“no preference” indicates the author’s willingness to be placed in the
best format for the program.
Each paper or abstract requires one sponsoring attendee (i.e. someone
who registered and is attending the conference). A single attendee can
not sponsor more than two abstracts or papers.
Oral presentations will be selected from both full length papers and
abstracts.
*** Post-Conference Journal Publication ***
The BIH conferences have the formal ties with Brain Informatics
journal (Springer,https://www.springer.com/40708). Accepted papers
from the conference, including their Best Paper Award papers, will be
expended and revised for possible inclusion in the Brain Informatics
journal each year. It is fully sponsored and no any
article-processing fee charged for BIH authors.
Selected submissions will be considered for publication in special
issues of international journals after their papers are extended to a
full-length paper and pass a review process. More information can be
found athttps://www.bih-amt.com/publications/
*** Topics and Areas ***
Please find the topics and areas of interest of the 2015 International
Conference on Brain Informatics and Health (BIH’15)
athttps://www.bih-amt.com/call-for-papers/topics/
*** AMT’15 Session ***
The advance of wearable sensor technology makes the monitoring of
human behavior and life style becomes feasible. This development gives
the active media technology a new dimension which is more closely
related to the healthcare and cognitive studies. Following the success
of past conferences in this series, AMT’15 will be jointly held with
BIH’15 as a special session.
ORGANIZERS
==========
General Chairs:
Karl Friston, University College London, UK
Yike Guo, Imperial College London, UK
Program Chairs:
Aldo Faisal, Imperial College London, UK
Sean Hill, EPFL, Switzerland
Hanchuan Peng, Allen Institute for Brain Science, US
Workshop/Special-Session Chairs:
Andreas Holzinger, Medical University Graz, Austria
Zhisheng Huang, Vrije University of Amsterdam, Netherlands
David Powers, Flinders University of South Australia, Australia
Publicity Chairs:
Jessica Turner, Georgia State University, US
Juan D. Velasquez, University of Chile, Chile
Yi Zeng, Institute of Automation, CAS, China
Local Organizing Chairs:
Thomas Henis, Imperial College London, UK
Kai Sun, Imperial College London, UK
Chao Wu, Imperial College London, UK
Exhibition/Sponsorship Chair:
Caroline Li, University Kent, UK
Steering Committee Co-Chairs:
Ning Zhong, Maebashi Institute of Technology, Japan
Jiming Liu, Hong Kong Baptist University, Hong Kong
*** Contact Information ***
Chao Wu, Imperial College London, UK
<c.wu09@imperial.ac.uk>
Aldo Faisal, Imperial College London, UK
<a.faisal@imperial.ac.uk>