[UAI] Research fellow/PostDoc position available: Machine Learning & Bioinformatics

[Claudio Angione]

Dear all,

We currently have a  Research Fellow position available in Machine Learning
& Bioinformatics.

Salary: £37,345 - £50,132

Desirable - but not essential - PhD degree (computer science or related
subjects, e.g. bioinformatics, mathematics, physics, or engineering).

Application closing date: 18/12/2018

Direct link for applications:
https://recruitment.tees.ac.uk/itrentlive_webrecruitment/wrd/run/etrec107gf.open?VACANCY_ID=5577342kzw=3395700LFi=USA

Best wishes,

Claudio Angione, PhD (Cantab)

Senior Lecturer
Department of Computer Science & Information Systems
Teesside University (UK)

Webpage: *https://www.scm.tees.ac.uk/c.angione/
*
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[UAI] Call for Papers to the Second International Workshop on Multimedia Pragmatics

[William Grosky]

SECOND INTERNATIONAL WORKSHOP ON MULTIMEDIA PRAGMATICS (MMPrag'19)
Co-Located with the IEEE SECOND INTERNATIONAL CONFERENCE ON MULTIMEDIA
INFORMATION PROCESSING AND RETRIEVAL (MIPR'19)
March 28-30, 2019 - San Jose, California


Website: http://mipr.sigappfr.org/19/
IMPORTANT
DATES

NEW DATES(CONSIDER THIS):
Submissions due: January 25, 2019
Acceptance notification: February 1, 2019
Camera-ready: February 8, 2019
Workshop date: March 28, 2019
===DESCRIPTION=
Most multimedia objects are spatio-temporal simulacrums of the real world.
This supports our view that the next grand challenge
for our community will be understanding and formally modeling the flow of
life around us, over many modalities and scales. As
technology advances, the nature of these simulacrums will evolve as well,
becoming more detailed and revealing to us more
information concerning the nature of reality.

Currently, IoT is the state-of-the-art organizational approach to construct
complex representations of the flow of life around us.
Various, perhaps pervasive, sensors, working collectively, will broadcast
to us representations of real events in real time. It
will be our task to continuously extract the semantics of these
representations and possibly react to them by injecting some
response actions into the mix to ensure some desired outcome.

Pragmatics studies context and how it affects meaning, and context is
usually culturally, socially, and historically based. For
example, pragmatics would encompass the speaker’s intent, body language,
and penchant for sarcasm, as well as other signs, usually
culturally based, such as the speaker’s type of clothing, which could
influence a statement’s meaning. Generic signal/sensor-based
retrieval should also use syntactical, semantic, and pragmatics-based
approaches. If we are to understand and model the flow of
life around us, this will be a necessity.

Our community has successfully developed various approaches to decode the
syntax and semantics of these artifacts. The development
of techniques that use contextual information is in its infancy, however.
With the expansion of the data horizon, through the
ever-increasing use of metadata, we can certainly put all media on more
equal footing.

The NLP community has its own set of approaches in semantics and
pragmatics. Natural language is certainly an excellent exemplar of
multimedia, and the use of audio and text features has played a part in the
development of our field.

After a successful first workshop in Miami, we intend to continue the
tradition with the second workshop. Now is the perfect time
to continue to actively promote this cross-fertilization of our ideas to
solve some very hard and important problems.

==AREAS
Authors are invited to submit regular papers (6 pages), short papers (4
pages), and demo papers (4 pages) at
https://easychair.org/conferences/?conf=mmprag19. The workshop website is
mipr.sigappfr.org/19/.

Topics of interest include, but are not limited to:

- Affective computing
- Annotation techniques for images/videos/other sign-based modalities
- Computational semiotics
- Cross-cultural multi-modal recognition techniques
- Digital Humanities
- Distributional semantics
- Event modeling, recognition, and understanding
- Gesture recognition
- Human-machine multimodal interaction
- Integration of multimodal features
- Machine learning for multimodal interaction
- Multimodal analysis of human behavior
- Multimodal data modeling, datasets development, sensor fusion
- Multimodal deception detection
- Ontologies
- Sentiment analysis
- Structured semantic embeddings
- Word and feature embeddings - generation, semantic property discovery,
corpus dependencies
  sensitivity analysis, retrieval aids

To be included in the IEEE Xplore Library, accepted papers must be
registered and presented.


ORGANIZATION===
Chairs:
R. Chbeir, U of Pau, FR
W. Grosky, U Mich-D, US

Program Committee
W. Abd-Almageed, ISI, US
M. Abouelenien, U Mich-D, US
R. Agrawal, ITL, ERDC, US
A. Aizawa, NII, Japan
Y. Aloimonos, UMD, US
A. Belz, U of Brighton, UK
R. Bonacin, CTI, BR
J.L. Cardoso, CTI, BR
F. de Franca, UFABC, BR
J. Hirschberg, Columbia U, US
D. Hogg, U of Leeds, UK
A. Jadhav, IBM, US
C. Leung, HK Baptist U, HK
D. Martins, UFABC, BR
A. Pease, Infosys, US
J. Pustejovsky, Brandeis, US
T. Ruas, U Mich-D, US
V. Rubin, UWO, CA
S. Satoh, NII, Japan
A. Sheth, Wright St U, US
P Stanchev, Kettering U, US
J. Tekli, American U, LEB

-- 
William Grosky
Professor
Department of Computer and Information Science
University of Michigan-Dearborn

[UAI] CFP IEEE TNNLS Special Issue on Deep Representation and Transfer Learning for Smart and Connected Health

[Ariel Ruiz-Garcia]

Call for Papers:
IEEE Transactions on Neural Networks and Learning Systems

Special Issue on Deep Representation and Transfer Learning for Smart and 
Connected Health


Important Dates

31 March 2019 - Deadline for manuscript submission

30 June 2019 - Reviewer's comments to authors

31 August 2019 - Submission of revised papers

31 October 2019 - Final decision of acceptance

30 November 2019 - Camera-ready papers

December 2019- Tentative publication date



Aims and Scope:



Deep neural networks (DNNs) are one of the most efficient learning systems. 
However, determining how to best learn a set of data representations that are 
ideal for a given task remains a challenge.  Representation and Transfer 
Learning (RTL) can facilitate the learning of complex data representations by 
improving the generalization performance of DNNs, and by taking advantage of 
features learned by a model in a source domain, and adapting the model to a new 
domain. Nonetheless, contemporary theory in RTL is unable to deal with issues, 
such as: the inherent trade-off between retaining too much information from the 
input and learning universal features; limited data or changes in the joint 
distribution of the input features and output labels in the target domain; 
dataset bias, among others. Therefore, new theoretical mechanisms and 
algorithms are required to improve the performance and learning process of DNNs.

Smart and Connected Health (SCH), an emerging and complex domain, can benefit 
from new advancements in RTL. For instance, RTL can overcome the limitations 
imposed by the lack of labelled data in SCH by (i) training a model to learn 
universal data representations on larger corpora in a different domain, and 
then adapting the model for use in a SCH context, or (ii) by using RTL in 
conjunction with generative neural networks to generate new healthcare-related 
data. Nonetheless, the use of RTL in designing SCH applications requires 
overcoming problems such as rejection of unrelated information, dataset bias or 
neural network co-adaptation.

This special issue invites novel contributions addressing theoretical aspects 
of representation and transfer learning and theoretical work aimed to improve 
the generalization performance of deep models, as well as new theory attempting 
to explain and interpret both concepts. State-of-the-art works on applying RTL 
for developing smart and connected health intelligent systems are also 
welcomed. Topics of interest for this special issue include but are not limited 
to:

Theoretical Methods:

*  Distributed representation learning;

*  Transfer learning;

*  Invariant feature learning;

*  Domain adaptation;

*  Neural network interpretability theory;

*  Deep neural networks;

*  Deep reinforcement learning;

*  Imitation learning;

*  Continuous domain adaptation learning;

*  Optimization and learning algorithms for DNNs;

*  Zero and one-shot learning;

*  Domain invariant learning;

*  RTL in generative and adversarial learning;

*  Multi-task learning and Ensemble learning;

*  New learning criteria and evaluation metrics in RTL;

Application Areas:

*  Health monitoring;

*  Health diagnosis and interpretation;

*  Early health detection and prediction;

*  Virtual patient monitoring;

*  RTL in medicine;

*  Biomedical information processing;

*  Affect recognition and mining;

*  Health and affective data synthesis;

*  RTL for virtual reality in healthcare;

*  Physiological information processing;

*  Affective human-machine interaction;


Guest Editors

Vasile Palade, Coventry University, UK

Stefan Wermter, University of Hamburg, Germany

Ariel Ruiz-Garcia, Coventry University, UK

Antonio de Padua Braga, University of Minas Gerais, Brazil

Clive Cheong Took, Royal Holloway (University of London), UK



Submission Instructions

  1.  Read the Information for Authors at 
https://cis.ieee.org/publications/t-neural-networks-and-learning-systems.
  2.  Submit your manuscript at the TNNLS webpage 
(http://mc.manuscriptcentral.com/tnnls)
 and follow the submission procedure. Please, clearly indicate on the first 
page of the manuscript and in the cover letter that the manuscript is submitted 
to this special issue. Send an email to the guest editor Vasile 

[UAI] CFP: Big Data Visual Exploration and Analytics Workshop (BigVis 2019)

[Nikos Bikakis]

 
Call for Papers



BigVis 2019 :: 2nd International Workshop on Big Data Visual Exploration and 
Analytics
  https://bigvis.imsi.athenarc.gr/bigvis2019
  EDBT/ICDT 2019, March 26, 2019, Lisbon, Portugal
   
Held in conjunction with the 22nd Intl. Conference on Extending Database Technology & 22nd Intl. Conference on Database Theory (EDBT/ICDT 2019)


In the Big Data era, the growing availability of a variety of massive datasets 
presents challenges and opportunities to not only corporate data analysts but 
also others, such as research scientists, data journalists, policy makers, 
SMEs, and individual data enthusiasts datasets are typically: accessible in a 
raw format that are not being loaded or indexed in a database (e.g., plain 
text, json, rdf), dynamic, dirty and heterogeneous in nature. The level of 
difficulty in transforming a data-curious user into someone who can access and 
analyze that data is even more burdensome now for a great number of users with 
little or no support and expertise on the data processing part. The purpose of 
visual data exploration is to facilitate information perception and 
manipulation, knowledge extraction and inference by non-expert users. 
Interactive visualization, used in a variety of modern systems, provides users 
with intuitive means to interpret and explore the content of the data, identify 
interesting patterns, infer correlations and causalities, and supports 
sense-making activities that are not always possible with traditional data 
analysis techniques.

In the Big Data era, several challenges arise in the field of data 
visualization and analytics. First, the modern exploration and visualization 
systems should offer scalable data management techniques in order to 
efficiently handle billion objects datasets, limiting the system response in a 
few milliseconds. Besides, nowadays systems must address the challenge of 
on-the-fly scalable visualizations over large and dynamic sets of volatile raw 
data, offering efficient interactive exploration techniques, as well as 
mechanisms for information abstraction, sampling and summarization for 
addressing problems related to visual information overplotting. Further, they 
must encourage user comprehension offering customization capabilities to 
different user-defined exploration scenarios and preferences according to the 
analysis needs. Overall, the challenge is to enable users to gain value and 
insights out of the data as rapidly as possible, minimizing the role of 
IT-expert in the loop.

The BigVis workshop aims at addressing the above challenges and issues by providing 
a forum for researchers and practitioners to discuss exchange and disseminate their 
work. BigVis attempts to attract attention from the research areas of Data 
Management & Mining, Information Visualization and Human-Computer Interaction 
and highlight novel works that bridge together these communities.



Workshop Topics
---
In the context of visual exploration and analytics, topics of interest include, 
but are not limited to:
 - Visualization and exploration techniques for various Big Data types (e.g., 
stream, spatial, high-dimensional, graph)
 - Human-centered database techniques
 - Indexes and data structures for data visualization
 - In situ visual exploration and analytics
 - Progressive visual analytics
 - Interactive caching and prefetching
 - Scalable visual operations (e.g., zooming, panning, linking, brushing)
 - Big Data visual representation techniques (e.g., aggregation, sampling, 
multi-level, filtering)
 - Setting-oriented visualization (e.g., display resolution/size, smart phones, 
pixel-oriented, visualization over networks)
 - User-oriented visualization (e.g., assistance, personalization, 
recommendation)
 - Visual analytics (e.g., pattern matching, timeseries analytics, prediction 
analysis, outlier detection, OLAP)
 - Immersive visualization and visual analytics
 - Visual and interactive data mining
 - Models of human-in-the-loop data analysis
 - High performance/Parallel techniques
 - Visualization hardware and acceleration techniques
 - Linked Data and ontologies visualization
 - Case and user studies
 - Systems and tools
 
 
Submissions

---
 - regular research papers (up to 8 pages)
 - work-in-progress papers (up to 4 pages)
 - vision papers (up to 4 pages)
 - system papers and demos (up to 4 pages)


Important Dates
---
  Submission: January 4, 2019
  Notification: January 22, 2019
  Camera-ready: January 29, 2019
  Deadlines expire at 5pm PT
  Workshop: March 26, 2019  


Organizing Committee
---
  Nikos Bikakis, University of Ioannina, Greece
  Kwan-Liu Ma, University of California-Davis, USA  
  Olga Papaemmanouil, Brandeis University, USA
  George Papastefanatos, ATHENA Research Center, Greece
  


Special Issue
---
  Extended versions of the best papers of 

[UAI] Call for Applications

[Jan Peters]

Robotics & Machine Learning Positions
===

The Intelligent Autonomous Systems Labs (IAS) at the Technical University
of Darmstadt (TU Darmstadt) is seeking for several

*highly qualified postdoctoral researchers.*

(exceptionally talented Ph.D. students will also be considered)
with strong interest in one or more of the following research
topics:

* Robot Learning (especially Robot Reinforcement Learning,
Imitation, and Model Learning)
* Robot Grasping and Manipulation
* Robot Control, Learning for Control
* Robot Table Tennis

Please relate clearly to these topic in your Research Statement.
Note that we currently can only consider PhD students with real
robot experience (for post-docs, we are more open-minded).

Outstanding students and researchers from the areas of robotics and
robotics-related areas including machine learning, control engineering
or computer vision are welcome to apply. The candidates are expected
to conduct independent research and at the same time contribute to
ongoing projects in the areas listed above. Successful candidates can
furthermore be given the opportunity to work with undergraduate, M.Sc.
and Ph.D. students.

Due to our strong ties to the Max Planck Institute for Intelligent
Systems, many companies (ABB, Bosch Centre for AI, Honda Research
Institute, Intel, NVIDEA NVAIL, Porsche Motor Sports, VW AI Lab, etc)
there will be ample opportunities of collaboration with these institutes.



ABOUT THE APPLICANT
Ph.D. position applicants need to have a Master's degree in a
relevant field (e.g., Robotics, Computer Science, Engineering,
Statistics & Optimization,  Math and Physics) and have exhibited
their ability to perform research in either robotics or machine
learning.

A successful Post-doc applicant should have a strong robotics and/or
machine learning background with a track record of top-tier research
publications, including relevant conferences (e.g., RSS, ICRA, IROS or
ICML, IJCAI, AAAI, NIPS, AISTATS) and journals (e.g., AURO, TRo,
IJRR or JMLR, MLJ, Neural Computation) . A Ph.D. in Computer Science,
Electrical or Mechanical Engineering (or another field clearly related
to robotics and/or machine learning) as well as strong organizational
and coordination skills are a must.

Expertise in working with real robot systems is a big plus for all
applicants.



THE POSITIONS
The positions are started with a 24 months contract and may be
extendable up to 48 months. Payment will be according to the German
TVL E-13 or E-14 payment scheme, depending on the candidates
experience and qualifications.



HOW TO APPLY?
All complete applications submitted through our online application
system found at

http://www.ias.tu-darmstadt.de/Jobs/Application

will be considered. There is no fixed deadline: the positions will be
filled as soon as possible. Ph.D. applicants should provide at least a
research statement, a PDF with their CV, degrees, and grade-sheets,
and two references who are willing to write a recommendation letter.
PostDoc applicants require three references and, in addition,  should
provide their top three publications. Please ensure to include a link
to your research web-site as well as your date of availability.



ABOUT IAS
The Intelligent Autonomous Systems Lab (IAS) aims at endowing robots with
the ability to learn new tasks and adapt their behavior to their
environment. To accomplish this goal, IAS focuses on the intersection
between Machine Learning, Robotics and Biomimetic Systems. Resulting
research topics range from algorithm development in machine learning
over robot grasping/manipulation and robot table tennis to biomimetic
motor control/learning and brain-robot interfaces. Members of CLAS and IAS
have been highly successful, as exhibited by recent awards, which include a
Daimler Benz Fellowship, several Best Cognitive Robotics Paper Awards, the
Georges Giralt Best 2013 Robotics PhD Thesis Award, an IEEE RAS Early
Career Award, etc. The lab collaborates with numerous universities in
Germany, Europe, the USA and Japan. IAS is partner in several
European projects with many top institutes in ML and Robotics..

Our lab member have been *EXCEPTIONALLY* successful. All our former postdocs
have been offered faculty positions and five of our 14 graduated PhD
students have taken on faculty jobs.

The IAS lab is located in the Robert Piloty Building in the beautiful
Herrngarten park. It is less than fifty meters from a beer garden
frequently used for lab meetings and after successful paper submissions.


ABOUT TU DARMSTADT
The TU Darmstadt is one of the top technical universities in
Germany, and is well known for its research and teaching. It was
one of the first universities in the world to introduce programs in
electrical engineering. our chemical elements were discovered at
Darmstadt, most prominently, the element darmstadtium, and it is
Germany's first fully autonomous university. More information can be
found on: 

[UAI] [post-doc] position open in Visual SLAM

[Wang Han (Assoc Prof)]

The Nanyang Technological University, Singapore invites applications for one 
postdoctoral research fellow position to participate in the development of  
autonomous vehicle driving using vision and AI.



Applicants for the postdoc research fellow position should hold a Ph.D degree 
in one or more of the following areas:



1)  Navigation

2)  SLAM

3)  Vision

4)  AI



The applications should have a track record of competitive research experience 
in terms of journal publications and have a good command of English and are 
able to communicate well.



Application Procedure:

Suitably qualified candidates are invited to submit a CV, cover letter 
initially. Short-listed candidates will be notified for submission of full 
application packages. Electronic submission of application is encouraged and 
can be sent to:

Dr. Wang Han, Associate Professor
School of EEE, S2
Nanyang Technological University, Singapore 639798
E-Mail: h...@ntu.edu.sg
Tel.: (65) 6790-4506
Fax: (65) 6792-0415
http://www3.ntu.edu.sg/home/hw/



CONFIDENTIALITY: This email is intended solely for the person(s) named and may 
be confidential and/or privileged. If you are not the intended recipient, 
please delete it, notify us and do not copy, use, or disclose its contents.
Towards a sustainable earth: Print only when necessary. Thank you.
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[UAI] 2 Faculty positions Explainable Artificial Intelligence at TU Delft

[Catholijn Jonker - EWI]

We are looking for two faculty members to strengthen our research in 
Explainable Artificial Intelligence in the field of Human-Agent collaboration 
at TU Delft.
Relevant topics of research include, but are not limited to, social robotics, 
conversational agents, affective computing, normative, reflective and strategic 
reasoning by applying Knowledge Representation techniques and/or Machine 
Learning. Candidates working on related challenges are also welcome to apply.
The positions are at Assistant or Associate Professor level.
For more information see
https://vacature.beta.tudelft.nl/vacaturesite/permalink/51218/?lang=en

or contact Catholijn Jonker - EWI 
(c.m.jon...@tudelft.nl) or Willem-Paul Brinkman 
(w.p.brink...@tudelft.nl)


Prof. Dr. C.M. Jonker
Interactive Intelligence Group, Fac. EEMCS, TU Delft
VanMourikBroekmanweg 6, Delft
Building number: 28 Office: West6.600
Office phone: +31.15.2781315
Mobile phone: +31.6.48875207
home: http://ii.tudelft.nl/~catholijn




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[UAI] Call for Book Chapters on Deep Biometrics

[Richard Jiang]

Dear Colleagues,



We would like to invite you to contribute a chapter for the upcoming volume 
entitled “Deep Biometrics” to be published by Springer, the largest global 
scientific, technical, and medical ebook publisher. The volume will be 
available both in print and in ebook format by late 2018/early 2019 on 
SpringerLink, one of the leading science portals that includes more than 8 
million documents, an ebook collection with more than 160,000 titles, journal 
archives digitized back to the first issues in the 1840s, and more than 30,000 
protocols and 290 reference works.



Below is a short description of the volume:

Recent development in machine learning, particularly deep learning, has brought 
out drastic impact on Biometrics, which is a classic topic to utilize Machine 
Learning for biometric identification. Particularly, Deep Learning can benefit 
from the training with large unlabelled datasets via semi-supervised or 
unsupervised learning.



This book aims to highlight recent research advances in biometrics using 
semi-supervised and unsupervised new methods such as Deep Neural Networks, Deep 
Stacked Autoencoder, Convolutional Neural Networks, Generative Adversary 
Networks, Ensemble Methods, and so on, and exploit these novel methods in the 
emerging new areas such as privacy and security issues, cancellable biometrics 
and soft biometrics, smart cities, big biometric data, biometric banking, 
medical biometrics, and healthcare biometrics, etc..



The goal of this volume is to summarize the recent advances in using Deep 
Learning in the area of biometric security and privacy. Topics of interest 
include: (but not limited to)

• Deep Learned Biometric Features

• Convolutional Neural networks

• Deep Stacked Autoencoder

• Deep Face Detection

• Deep Gait Recognition

• Biometrics in Cybersecurity

• Biometrics in Cognitive Robot

• Healthcare Biometrics

• Medical Biometrics

• Biometrics in Social Computing

• Biometric Block Chain

• Privacy and Security Issues

• Iris, Fingerprints, DNA, Palmprints

• Gait, EEG, Heart rates

• Multimodal Fusion

• Soft Biometrics

• Cancellable Biometrics

• Big data issues in Biometrics

• Biometrics for Internet of things

Each contributed chapter is expected to present a novel research study, a 
comparative study, or a survey of the literature. Note that there will be no 
publication fees for accepted chapters.



Important Dates:

  Submission of abstracts: as soon as possible

  Notification of initial editorial decisions: 2-3 days after abstract 
submission

  Submission of full-length chapters Dec 15, 2018

  Notification of final editorial decisions Jan 15, 2019

  Submission of revised chapters Feb 15, 2019



All submissions should be done via EasyChair:

  https://easychair.org/conferences/?conf=deepbio2019

Original artwork and a signed copyright release form will be required for all 
accepted chapters. For author instructions, please visit:

  http://www.springer.com/authors/book+authors?SGWID=0-154102-12-417900-0

Please feel free to contact us via email 
(perceptualscie...@outlook.com, or any 
editors below) regarding your chapter ideas.


Editorial Board

• Dr Richard Jiang

   Computer and Information Sciences,

   Northumbria University, United Kingdom

   Email: richard.ji...@unn.ac.uk

• Professor Chang-Tsun Li

   School of Computing and Mathematics,

   Charles Sturt University, Australia

   Email: c...@csu.edu.au

• Dr Weizhi Meng

   Applied Mathematics & Computer Science

   Technical University of Denmark, Denmark

   Email: w...@dtu.dk

• Professor Christophe Rosenberger

   Computer Security

   ENSICAEN – GREYC, France

   Email: 
christophe.rosenber...@ensicaen.fr



Contact:

All questions about submissions can be emailed to 
perceptualscie...@outlook.com or any 
editor in the board.



Many thanks!



Kind Regards,

Editors of the Book













































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