PhD fellowship at York funded by DSTL

One fully funded PhD fellowship for four years starting
October 2012 is available in the area of

Identifying Human Activities from Video Sequences

under the supervision of Dr. Adrian G. Bors,
Department of Computer Science, University of York, UK.
You will be part of a research group which is well known
internationally in a top rated department in UK.
The PhD fellowship is funded by DSTL and will provide
a salary for up to four years while covering university fees.

The PhD candidate will be expected to develop and implement
new methodologies for processing and analyzing video sequences
showing various human activities. He/she will be required
to develop computational methods for extracting characteristic
features, detect and track human motion. At the higher processing
level, the PhD candidate will develop methods used for
classifying and detecting human activities from image sequences.
This PhD project will require writing research reports and scientific
papers as well as communicating and presenting research results
to DSTL and to the scientific community.

This PhD fellowship is available only for citizens of EU countries.
You will be expected to have an MSc or a good first degree
in one of the fields: Computer Science, Electrical and Electronics
Engineering, Mathematics and Applied Mathematics, or Physics
and have a strong interest in scientific research.
You should have knowledge or a strong willingness to learn quickly
the following:

* Programming skills in Matlab and C
* Good knowledge and understanding of algorithms and
of the mathematics behind them
* Good knowledge of written and spoken English
* Ability to write scientific papers and reports as well as
to present and demonstrate research results

It would be expected that your MSc or final year BSc project was
in an area related to Computer Vision, Pattern Recognition,
Image Processing or Computational Intelligence.

Knowledge and experience with the following would be
highly desirable:

* Processing and analyzing images and image sequences
* Knowledge of applied statistics and mathematics
* Graph representation of data
* Numerical assessment and analysis of experimental results

If you are an EU country citizen and consider yourself as
a suitable candidate for this DSTL funded PhD fellowship
you should send the following by email to adrian.bors@york.ac.uk:

– Your CV
– Short statement of your interest and how would you approach
this research topic
– Short description of your final year or MSc project
– Transcripts with marks achieved during your previous study
– List of scientific papers published or submitted, if any
– Other relevant major achievements
– Names of two academic persons who can provide references
for you if requested

PhD funding available: 4 years

Start date: October 2012

Location: Dept. of Computer Science
University of York
York, UK

Contact: Dr. Adrian G. Bors
E-mail: adrian.bors@york.ac.uk

http:\www-users.cs.york.ac.uk/~adrian/

All candidates will be considered for a preliminary selection
and those with the strongest profiles will be contacted.

Fully-funded 4-year PhD studentship in in “Statistical Signal Processing and Machine Learning for Network Traffic Anomaly Detection”

* £17,000 per annum tax-free stipend + full UK/EU fees + annual conference travel budget.

* Applicants must be UK/EU nationals.

* The candidate must start October 2012.

* Application Deadline: 31st July 2012.

This project will develop advanced multivariate statistical and machine learning methodology for the analysis of network traffic measurement data. The aim is to deliver robust detection of hostile cyber activity. Several open problems will be addressed, including the choice of background model, features, and classifiers; and possible incorporation of multiresolution inferential methodology, which may include extensions to wavelet-Bayesian Markov chain Monte Carlo change point approaches.

The ideal candidate will have a strong interest in exciting recent developments in the convergent disciplines of computational statistics, machine learning, and signal processing.

The research will be undertaken within the UCL Security Science Doctoral Training Centre (UCL SECReT) see and the UCL Dept of Statistical Science . The research is funded by the UK Ministry of Defence and will involve some collaboration with the Defence Science and Technology Laboratory.

For informal enquiries, please contact the principal supervisor, Dr. James Nelson, UCL Department of Statistical Science: .

For information on how to apply, please see:

www.ucl.ac.uk/secret/secret_news/statistical-signal

Last Call for Papers: Silver 2012 – International workshop on Learning from Unexpected Results, collocated with ECML-PKDD 2012, August 24-28, Bristol, United Kingdom

Workshop website and call for papers

The Silver Lining: learning from unexpected results
With this workshop, we want to give a voice to unexpected results that deserve wider dissemination: thoroughly conducted studies that follow a plausible idea that did not achieve the aspired results, but instead taught us novel lessons; studies showing that well-known (successful) methods will not work under certain conditions, highlighting remaining weaknesses and new avenues of research; and stories that focus on how a successful method was discovered after one or several failed attempts.

Unexpected results chart the boundaries of our knowledge: they identify errors, reveal false assumptions, and force us to dig deeper. Unfortunately, this process is rarely mentioned in the machine learning and data mining discourse. Indeed, there exists a publication bias that favors (incremental) successes over novel discoveries of why some ideas, while intuitive and plausible, do not work.

With Silver, we invite original studies that fall in one of the following categories:
 unexpected results: thoroughly conducted studies that follow an intuitive and plausible idea that made perfect sense but did not achieve the aspired results. They should explore the underlying reasons for the observed results and highlight what we can learn from them;
 counterexamples: studies showing that well-known (successful) methods will not work under certain conditions, highlighting remaining weaknesses and new avenues of research;
 the road to success: stories that focus on how a successful method was discovered after one or several failed attempts, highlighting how a negative was turned into a positive;
 other papers, e.g. position statements, as long as they are relevant to the overall aim of the workshop.
In addition, extended abstracts may present position statements or interesting cases of (unexplained) surprising results for which the authors would like input from the community.
Important dates
 submission: June 29
 notification: July 20
 camera-ready version: August 3
 Silver 2012: September 24 (tentative date)

PhD position in visual object categorization

The Idiap Research Institute seeks qualified candidates for one PhD research position on computer vision and machine learning applied to robotics.

The PhD will focus on how to provide robots with categorisation models based on situated visual information and their associated affordances. The resulting algorithms should be able to learn continuously over time about new perceptual and semantic inputs, as well as new category models by exploiting existing priors. The research will be conducted within the context of a recently funded Swiss project including European partners.

The ideal candidate should hold a Master degree in Electrical Engineering or Computer Science in the field of machine learning, computer vision, robotics or pattern recognition. Excellent mathematical and programming skills are expected. The successful PhD student will be enrolled in the EPFL doctoral program. Appointment for the PhD position is for a maximum of 4 years.

The starting date is fall 2012. For further details about the position please contact: Dr. Barbara Caputo.
—
Dr. Barbara Caputo, Senior Researcher

IDIAP Research Institute
Centre du Parc
P. O. Box 592
rue Marconi 19,
CH- 1920 Martigny
Switzerland

tel: +41 277 217 737
fax: +41 277 217 712
url: http://www.idiap.ch/~bcaputo
email: bcaputo@idiap.ch

The Second International Workshop on Mining Communities and People Recommenders (COMMPER 2012)

In conjunction with ECML/PKDD 2012
Bristol, UK,
Friday, September 28, 2012

http://research.ics.tkk.fi/events/commper2012/
Paper deadline: June 29, 2012

======================================================

[[ Call For Papers ]]

Data mining and knowledge discovery in social networks has advanced significantly over the past several years, due to the availability of a large variety of online and online social network systems. The focus of COMMPER is on two main streams of social networks: community mining and system recommenders.

The first focus of this workshop is on mining communities in social networks and in particular in scientific collaboration networks.
Consider, for example, a dataset of scientific publications along with information about each publication and the complete citation network.
Many data-analysis questions arise: what are the underlying communities, who are the most influential authors, what are the set-skills of individual authors, what are the observed collaboration patterns, how does interest on popular topics propagates, who does the network evolve in terms of collaborations, topics, citations, and so on. In this workshop we indent to bring domain experts, such as bibliometricians, closer to researchers from the fields of data mining and social networks. The expected outcome is to strengthen the collaboration of these communities aiming at high impact-research contributions and discussions. We aspire that the workshop will lead to the development of new insights and data mining methodologies that could be employed for the analysis of communities, models of human collaboration, topic discovery, evolution of social networks, and more.

People recommenders, the second main topic of this workshop, deal with the problem of finding meaningful relationships among people or organizations. In online social networks, relationships can be friends on Facebook, professional contacts on LinkedIn, dates on an online dating site, jobs or workers on employment websites, or people to follow on Twitter. The nature of these domains makes people-to-people recommender systems to be significantly different from traditional item-to-people recommenders. One basic difference in the people recommender domain is the benefit or requirement of reciprocal relationships. Another difference between these domains is that people recommenders are likely to have rich user profiles available. The goal of this workshop is to build a community around people recommenders and instigate discussion about this emerging area of research for recommender systems. With this workshop, we want to reach out to research done in both academia and industry.

[[ Topics ]]

We encourage that papers submitted to COMMPER focus on, but are not limited to the following topics:

* analysis of scientific communities;
* collaboration networks;
* bibliometrics and data mining;
* analysis of co-authorship networks;
* analysis of citation networks;
* communities in social networks;
* dynamic networks;
* formation of teams;
* learning skills of individuals;
* topic and community evolution and dynamics;
* comparative studies of community networks;
* people recommendation in social networks;
* community recommendations in social networks;
* mentor/mentee recommendations in tutoring systems;
* expert search and expertise recommendation;
* employee/employer recommendations;
* online dating recommendations;
* people search in the enterprise;
* team recommendations;
* reviewer assignment;
* location-aware people recommendation.

[[ Important Dates ]]

Paper Submission: June 29, 2012
Notification of Acceptance: July 20, 2012 Camera-ready Paper Due: August 3, 2012 Full-day Workshop at ECML/PKDD, Bristol, UK: September 28, 2012

[[ Submission Information ]]

The paper length can be up to 4 pages (short papers) and 8 pages (regular papers).
We welcome both novel research papers and work-in-progress papers. The submitted papers must be formatted according to the Springer-Verlag Lecture Notes in Artificial Intelligence guidelines.

All submitted manuscripts will be subject to peer-review by at least three members of the Program Committee.

Accepted contributions will be presented in the workshop and will be published in edited proceedings. The proceedings will be made available to all participants of the workshop.

Papers submitted to this workshop must not have been accepted for publication elsewhere or be under review for another workshop, conference, or journal.

[[ Workshop Organizers ]]

* Jaakko Hollmen, Aalto University, Finland.
* Panagiotis Papapetrou, Aalto University, Finland.
* Luiz Augusto Pizzato, University of Sydney, Australia.

[[ Program Committee ]]

* Shlomo Berkovsky, CSIRO, Australia
* Aristides Gionis, Yahoo! Research, Spain
* Dimitrios Gunopulos, University of Athens, Greece
* Jaakko Hollmén, Aalto University, Finland
* Irena Koprinska, University of Sydney, Australia
* Theodoros Lappas, Boston University, USA
* Radhakrishnan Nagarajan, University of Arkansas, USA
* Panagiotis Papapetrou, Aalto University, Finland
* Irma Pasanen, Aalto University, Finland
* Luiz Augusto Pizzato, University of Sydney, Australia
* Antti Ukkonen, Yahoo! Research, Spain

[[ Invited Speaker ]]

TBA

Course for the “Pattern Recognition in Neuroimaging Toolbox”, aka. PRoNTo – UCL, London, 5 July 2012

Dear colleagues,

We are pleased to announce that registration for the next PRoNTo(*) course is now open.

This one-day course will be held for the second time in London on July 5, and will offer a comprehensive coverage of all “Pattern Recognition in Neuroimaging Toolbox” features and functionalities, including:
– introduction to neuroimaging and machine learning concepts
– specific pattern recognition methods for neuroimaging
– software demonstration and hands on session

For further information and registration, please see:
http://www.mlnl.cs.ucl.ac.uk/pronto/prtcourses.html

Sincerely,
C. Phillips and J. Mourao-Miranda, on behalf of the PRoNTo team.

(*)
PRoNTo aims to facilitate the interaction between the machine learning and neuroimaging communities. The toolbox provides a variety of tools for the neuroscience and clinical neuroscience communities, enabling these communities to ask new questions that cannot be easily investigated using existing statistical analysis tools. On the other hand the machine learning community can easily contribute to the toolbox with novel published machine learning algorithms. PRoNTo is directly compatible with SPM8. See http://www.mlnl.cs.ucl.ac.uk/pronto/ for more details and download.
The poster presented at the OHBM2012 conference is also available here:
https://ww4.aievolution.com/hbm1201/files/content/abstracts/97421/0391_Schrouff.pdf

PASCAL Visual Object Classes Recognition Challenge 2012

We are running the PASCAL Visual Object Classes Recognition Challenge for a final time in its current form this year. There are competitions in object category classification, detection, and segmentation, and still-image action classification. A new taster competition has been introduced on action classification from approximate localization, and there is a taster competition on person layout (detecting head, hands, feet). There is also an associated large scale visual recognition taster competition organized by www.image-net.org.

We remind users that the PASCAL VOC Challenge evaluation server is available at http://host.robots.ox.ac.uk:8080/ and is currently providing evaluations and downloads for the VOC 2008-2012 challenges.

The development kit (Matlab code for evaluation, and baseline algorithms) and training/test data is now available at:

http://pascallin.ecs.soton.ac.uk/challenges/VOC/voc2012/index.html

where further details are given. The timetable of the challenge is:

* May 2012: Development kit (training and validation data plus
evaluation software) made available.

* 25 June 2012: Test set made available.

* 23 September 2012. Deadline for submission of results. There will be
no extensions.

* 12 October 2012: Workshop in association with ECCV 2012, Florence.

The workshop will have a new format this year, including talks from participants in the challenge, and invited talks from some of the most influential researchers in visual object recognition. See the workshop page for more
information:

http://pascallin.ecs.soton.ac.uk/challenges/VOC/voc2012/workshop/index.html

Mark Everingham
Luc Van Gool
Chris Williams
John Winn
Andrew Zisserman

Research Scientist – Data Mining / Learning, IBM Research Dublin

Job description

The Smarter Cities Technology Centre (SCTC) at the IBM Dublin Research Laboratory, Ireland is accepting applications for full-time researchers from entry-level through principal investigators.

We are hiring researchers in data mining, statistical analysis, predictive modeling, and machine learning. We are especially interested in candidates with strong background and experience in dealing with data, control, or planning of urban or environmental systems such as transportation and mobility services, sustainable energy (smart grid, renewable energy, intelligent buildings), and water management. Projects will involve identifying applications of data-intensive machine learning, invention of effective algorithms for these applications, and incorporation of these algorithms in distributed and parallel software systems.

The successful candidate will have demonstrated ability to define research plans, carry out leading research, and publish research results through professional journals, academic conferences, and patents. As a researcher you will be expected to organize challenging problems, develop new solutions, and work with business & development teams to ensure these solutions have a significant impact.

Required :
– Doctoral degree in Computer Science, Applied Mathematics, or Engineering
– Strong publication record in top-tier conferences and journals

The Smarter Cities Technology Centre is part of IBM Research and was created to take on the challenges of our increasingly urbanized world. We are an R&D organization that works closely with city authorities, universities, and small/large businesses to advance science & technology for smarter urban and environmental systems. We aim to expand our highly skilled and cross-disciplinary team to as many as 200 researchers and software engineers. Areas we currently focus on include: data mining and machine learning; statistical modeling & organization control and decision systems, social, semantic web; distributed information systems; knowledge & data engineering, geo-spatial analysis and organization simulation, real-time computing, and urban and environmental systems, e.g., water, energy, transportation.

We offer:
• A stimulating, cross-disciplinary environment in a leading research organization, • Opportunities for a unique combination of scientific and industrial research into real-world problems • Excellent ties to research groups worldwide • Up-to-date infrastructure and resources • The ability to take part in large-scale international research projects

Required
Doctorate Degree
At least 2 years experience in data mining, statistical analysis, predictive modeling or machine learning At least 2 years experience in software programming with C++ or Java
English: Fluent

Preferred
At least 1 year experience in SPSS, SAS, or similar statistical analysis or modeling tools At least 6 months experience in data mining or machine learning applied to urban or environmental systems

Additional information

https://jobs3.netmedia1.com/cp/job_summary.jsp?job_id=RES-0474477

http://www-05.ibm.com/ie/dublinresearchlab/

IBM is committed to creating a diverse environment and is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.

Action Recognition and Pose Estimation in Still Images Workshop at ECCV 2012, October 13, Firenze, Italy

http://vision.stanford.edu/apsi2012/index.html

Many human actions, such as “playing violin” and “taking a photo”, are well described by still images. Recognizing human actions and estimating human poses in still images will potentially provide useful information in image indexing and visual search, since a large proportion of available images contain people. Progress on these tasks is also beneficial to object and scene recognition, given the frequent human-object and human-scene interactions. Furthermore, as video processing algorithms often rely on some form of initialization from individual video frames, it would be interesting to have a better understanding of how, when, and to what extent static information can help recognize human actions and estimate human pose in videos. This workshop offers a great opportunity to bring together researchers and experts working on different aspects of action recognition and pose estimation to demonstrate their recent work. It provides a common playground for inspiring discussions and stimulating debates.

We invite high quality, original submissions for presentation during the workshop.
Contributions from the following areas are especially welcome:
• Human pose estimation in still images
• Human action recognition in still images • Modeling and recognition of human-object interactions • Scene context for human poses and actions • Understanding humans in videos or depth images • Novel datasets of human poses or actions • Actions and human pose research in cognitive psychology / human perception

Papers must be in PDF format and must not exceed 10 pages (ECCV format). All submissions are subject to a double-blind review process by the program committee.

• Deadline for submission of papers: July 3rd, 2012 • Notification of acceptance: August 2nd, 2012 • Camera-ready submission: August 8th, 2012 • Workshop date: October 13rd, 2012

Contact: apsi2012.ws@gmail.com

Workshop on Uncertainty Quantification for Climate and Environmental Models, UCL, 29 June 2012

As part of the PASCAL-funded Harvest program on “Uncertainty Quantification Pipeline for Climate Models”, a workshop on Uncertainty Quantification for Climate and Environmental Models will be held at University College London on 29 June 2012:
http://www.ucl.ac.uk/statistics/research/stochastic/workshop29June

Registration is necessary, and free, at:
http://harvestuq.eventbrite.co.uk