PASCAL2 | Knowledge 4 All Foundation Ltd.

July 11, 2011

PASCAL2

Data Mining School in Maastricht, The Netherlands

9-th Summer School on Dat Mining, Maastricht, The Netherlands
http://www.unimaas.nl/datamining/
An intensive 4-day introduction to methods and applications

Department of Knowledge Engineering, Maastricht University,
Maastricht, The Netherlands
August 29 – September 1, 2011

Introduction
Most business organizations collect terabytes of data about business
processes and resources. Usually these data provide just “facts and
figures”, not knowledge that can be used to understand and eventually
re-engineer business processes and resources. Scientific community in
academia and business have addressed this problem in the last 20 years
by developing a new applied field of study known as data mining.
In practice data mining is a process of extracting implicit,
previously unknown, and potentially useful knowledge from data. It
employs techniques from statistics, artificial intelligence, and
computer science. Data mining has been successfully applied for
acquiring new knowledge in many domains (like Business, Medicine,
Biology, Economics, Military, etc.). As a result most business
organizations need urgently data-mining specialists, and this is
the point where this school comes to help.

Description
The school curricullum is well balanced between theory and practice.
Each lecture is accompanied by a lab in which participants experiment
with the techniques introduced in the lecture. The lab tool is Weka, one
of the most advanced data-mining environments. A number of real data
sets will be analysed and discussed. In the end of the school
participants develop their own ability to apply data-mining techniques
for business and research purposes.

Tools
The school focuses on techniques with a direct practical use.
A step-by-step introduction to powerful (freeware) data-mining tools
will enable you to achieve specific skills, autonomy and hands-on
experience. A number of real data sets will be analysed and discussed.
In the end of the school you will have your own ability to apply data-
mining techniques for research purposes and business purposes.

Content
The school will cover the topics listed below.
– The Knowledge Discovery Process
– Data Preparation
– Basic Techniques for Data Mining:
+ Decision-Tree Induction
+ Rule Induction
+ Instance-Based Learning
+ Bayesian Learning
+ Support Vector Machines
+ Regression Techniques
+ Clustering Techniques
+ Association Rules
– Tools for Data Mining
– How to Interpret and Evaluate Data-Mining Results

Intended Audience
This school is intended for four groups of data-mining beginners:
students, scientists, engineers, and experts in specific fields who need
to apply data-mining techniques to their scientific research, business
management, or other related applications.

Prerequisites
The school does not require any background in databases, statistics,
artificial intelligence, or machine learning. A general background in
science is sufficient as is a high degree of enthusiasm for new
scientific approaches.

Certificate
Upon request a certificate of full participation will be provided after
the school.

Registration
To register for the school please send an email to:

smirnov(at)maastrichtuniversity.nl

In the e-mail please specify:
– Name
– University / Organisation
– Address
– Phone
-E-Mail

Registration Deadline: August 22, 2011

Registration fees
Academic fee 600 Euros
Non-academic fee 850 Euros

Included in the price are: school material and coffee breaks. The local
cafeteria will be available for lunch (not included).

Registration e-mail: smirnov(at)maastrichtuniversity.nl

Regular mail should be sent to:

Evgueni Smirnov
Department of Knowledge Engineering
Faculty of Humanities and Sciences
Maastricht University
P.O.Box 616
6200 MD Maastricht
The Netherlands
Phone: +31 (0) 43 38 82023
Fax: +31 (0) 43 38 84897
E-mail: smirnov(at)maastrichtuniversity.nl

July 11, 2011

PASCAL2

The PASCAL Probabilistic Inference Challenge (PIC11) – Call for Submissions

Probabilistic inference in graphical models is a key task in many applications, from machine vision to computational biology. Since the
problem is generally computationally intractable many approximations have been suggested over the years.

The goal of this challenge is to evaluate inference algorithms on difficult large scale problems.
Some of challenge highlights are:
– Algorithms for MAP, marginals, and partition function approximation will be evaluated.
– Solvers will be evaluated on different time scales (20 seconds, 20 minutes, 1 hour).
– An online leaderboard will show the relative rankings of the algorithms.
– Modest cash prizes will be available on all categories.
– After the challenge the evaluation site will remain active as a service to the community.

Timeline:
July 15th, 2011: Site open to registration and submission of solvers.
October 1st, 2011: Challenge Begins. No new models added after this date.
December 31st, 2011: Site closed to submissions.
January 15th, 2012: Winners announced.

Organizers: Gal Elidan and Amir Globerson (Hebrew University).
Web master: Uri Heinemann (Hebrew University)

Web Page: http://www.cs.huji.ac.il/project/PASCAL/

July 11, 2011

PASCAL2

PhD Studentship – Learning to Recognise Dynamic Visual Content from Broadcast Footage

Supervision: Dr Mark Everingham, University of Leeds

Deadline: Open until filled

Project Description:

A PhD studentship is available as part of an EPSRC funded project on “Learning to Recognise Dynamic Visual Content from Broadcast Footage” being jointly undertaken by the University of Leeds (Mark Everingham), the University of Oxford (Andrew Zisserman), and the University of Surrey (Richard Bowden).

The objective of the project is to develop automated tools that allow temporal visual content, such as a human gesturing, using sign language, or interacting with objects or other humans, to be learnt from standard TV broadcast signals using the transmitted annotation in the form of subtitles and annotation for the visually impaired as supervision. This requires the development of models of the visual appearance and dynamics of actions, and learning methods which can train such models using the weak supervision provided by the subtitles. Once the models have been learnt they can then be used without supervision, e.g. for sign language interpretation or automatic description of the content of video footage, and during the project demonstrators will be engineered for both of these applications.

The student will focus on the development of visual descriptors and learning algorithms for sign language and action recognition in broadcast video. S/he will be based in the School of Computing at the University of Leeds, and will be supervised by Dr Mark Everingham (http://www.comp.leeds.ac.uk/me/).

Funding Notes: The studentship is funded by an EPSRC project studentship and will start from 1st October 2011 or as soon as possible thereafter. The studentship is funded for 3 years and covers Home/EU fees and maintenance at the standard EPSRC rate (currently £13,590 per annum). Applications are welcome from overseas students, but such students would have to provide the difference between the UK/EU and the overseas student rates for university fees from some other source, such as a scholarship or personal funds.

Academic Staff Contact Details: Dr Mark Everingham. M.Everingham(at)leeds.ac.uk (for informal enquiries about the project only – do not send applications to this address).

Entry Requirements: The PhD candidate should have or expect to obtain a first class or strong 2.1 honours degree in computer science, mathematics, or related discipline. The following qualities are desirable: demonstrable experience in computer vision or machine learning; excellent record of academic and/or professional achievement; strong mathematical skills; strong programming skills, especially C/C++ and Matlab; good written and spoken communication skills in English.

Details on how to apply can be found at:

http://www.leeds.ac.uk/rds/prospective_students/apply/I_want_to_apply.html

Application Procedure: Formal applications for research degree study must be made either on line through the University website, or on the University’s application form. Detailed information of how to apply on line can be found at: http://www.leeds.ac.uk/students/apply_research.htm

The paper application form is available at: http://www.leeds.ac.uk/rsa/prospective_students/apply/I_want_to_apply.html

Please return the completed application form to the Research Degrees & Scholarships Office, University of Leeds, LS2 9JT.

Please note, if you intend to send academic references we can only accept them if they are on official letter headed paper and contain an original signature and stamp; they must arrive in sealed envelopes. Alternatively, the School will contact your named academic referees directly.

July 8, 2011

PASCAL2

Open PhD Position @ ETH Zurich “Wearable Assistant for Patients with Parkinson’s Disease”

=== Background ===
The Wearable Computing Laboratory at ETH Zurich (http://www.wearable.ethz.ch/) develops methods to recognize human activities and context from data captured from sensors placed on-body, such as those found in mobile phones.

One of the uses of context and activity recognition methods is to support independent living for people in need, such as elderly or people with disabilities.

=== CuPiD: “Closed-loop system for personalized and at-home rehabilitation of people with Parkinson’s Disease” ===

CuPiD is a European funded project with 8 partners distributed in 6 EU countries during a 3 year period (2011-2014). Partners include 2 universities, 2 medical centers, 3 enterprises and one research foundation.

The objective of CuPiD is to develop a system to support the rehabilitation of persons with Parkinson’s diseases. The project considers three issues commonly faced by Parkinson’s disease patients:
* Freezing of gait: while walking, the person suddenly “freezes” and is unable to pursue walking. This may last a few seconds up to a minute. The person is conscious of this, but unable to resume walking. This happens more oftenwhen performing sharp turns, entering narrow areas, or when faced with cognitive load or social pressure.
* Motor control: fine motor control used for manipulative gestures is often negatively affected with the progression of Parkinson’s disease
* Balance: loss of balance is common and may lead to falls, or difficulties in appropriately shifting the body weight in sit-to-stand transitions.

The aim of the CuPiD project is to devise wearable and ambient devices to perform at-home rehabilitation for these issues. The principle consists in providing pre-emptive feedback to the user, shortly before the onset of an issue, and to rely on neural plasticity for the user’s behavior to unconsciously adapt to avoid situations leading to these issues algogether.

CuPiD makes use of sensors such as movement sensors placed on body, physiological sensors, location awareness, etc, in order to recognize the onset of an issue. Feedback includes audio and vibrotactile feedback, and virtual reality. The CuPiD system allows logging of data to monitor the user’s rehabilitation progress by care personnel.

At ETH Zürich we focus within this project on the issue of the freezing of gait, but close collaboration is foreseen between all project partners as an integrated rehabilitation system is the ultimate goal of the project.

=== Job description ===

We offer a PhD position within the framework of the 3 year long (2011-2014) European-funded CuPid project.
In this position you will be responsible for one of the project’s work package. This work package consists in:
* devising methods and sensors to predict the upcoming onset of gait freeze from multimodal body-worn sensors data using data mining and pattern recognition techniques;
* devise appropriate audio feedback for the patients,
* combine sensing and feedback in dedicated embedded systems;
* conduct evaluations of the system with patients. This work is done in close collaborations with partners of the project.

Your work environment will be multinational, both in Zürich and with project partners within Europe, with frequent travels to the partner’s location.

Within this project, your research topics will include (but are not limited to):
* Multimodal data-mining: identification of relevant sensor signal patterns in a large dataset to predict onset of freeze
* Activity and context recognition with body-worn sensors
* User calibration techniques to adapt context recognition parameters to specific users
* Multimodal data fusion to combine location, motion, physiological signals to predict the onset of freeze
* Wireless sensor networks
* Laboratory and clinical evaluation: evaluations with patients at the partner’s clinical locations.

Starting date: ASAP

=== Requirements ===

The candidate has a diploma, MSc, or equivalent in electrical engineering, micro-engineering, computer science or mathematics.
He has strong interests in mobile computing, machine learning/pattern recognition, signal processing, adaptive and learning systems, and in the combination of theoretical and experimental research.
Fluent spoken and written English is mandatory.

=== Contact and application ===

For further information about the CuPiD project and your contribution within it, please contact Dr. Daniel Roggen (droggen(at)ife.ee.ethz.ch).

If you are interested and believe that you qualify, please send your application to Prof. Gerhard Tröster (troester(at)ife.ee.ethz.ch). Include:
* Curriculum Vitae with the names and contact details of at least 2 references
* a list of exams and grades obtained
* a cover letter explaining how your skills and research interests fit the project

For more information:
– http://www.wearable.ethz.ch/
– http://www.ife.ee.ethz.ch/openpositions/job_cupid1

July 8, 2011

PASCAL2

Open PhD Position @ ETH Zurich “Crowd-Sourcing of Human Activity Recognition on Mobile Phones”

=== Background ===

The Wearable Computing Laboratory at ETH Zurich (http://www.wearable.ethz.ch/) develops methods to recognize complex and hierarchical human activities from data captured from sensors placed on-body, such as those found in mobile phones.

One goal of our group is to achieve an automatic “life log” of the user’s activities using mobile phones sensors. An example of a life log could be: “yesterday, you were taking a coffee with your friend, then went for shopping, did cook a cake, and watched tv before going to sleep”. This has promising uses to support people with memory loss and dementia. It also enables a lot of exciting applications when combined with social networks, such as the automatic update of the user’s profile on facebook, and pervasive gaming.

In order to realize such a life log the system must be able to recognize location, modes of locomotion, postures, gestures, and infer higher-level activities from these primitives. This is done with streaming signal processing and machine learning techniques. Current approaches follow a “learning by demonstration” principle, where the user is requested to provide training data to the system. This is a major limitation currently, as the size of the training datasets remains small and does not capture the rich variability of human activities.

=== Smart-Days: “Smart Distributed daily living ActivitY-recognition Systems” ===

In a Swiss-funded project which involves ETH Zürich and the University of Applied Sciences atYverdon, we develop a novel crowd-sourcing-based approach to recognize complex human activities. The project time span is 2011-2014.

The Smart-DAYS system is composed of: multi-modal sensor nodes providing data relevant to the user’s activities and capable of local data interpretation (e.g. motion sensors nodes); an on-body mobile device (e.g. phone) that is fusing the sensor node data to infer the user’s activities; and a cloud server backend storing collective activity models.

Smart-Days offers these advances over the state of the art:
* The activity models are obtained from a multitude of users providing sporadic annotations about their current or past activities. Thus a large and rich set of human activities can be captured in a bottom-up process;
* The set of activities to recognize is not statically defined at design-time; it can grow to encompass new activities as they are discovered at run-time;
* The system is capable of adapting activity models at run-time to cope with changing user behavior patterns;
* The system can share and reuse the knowledge acquired between the user’s of the system;
* The system can exploit online data sources to bootstrap the recognition capabilities;
* Essentially, the system will be able to recognizing an unbounded, growable, and adaptable set of human activities in open-ended environments.

In order to realize this, Smart-Days approach combines:
* High-level activity recognition based on unsupervised hierarchical clustering and semisupervised techniques
* Crowdsourcing of activity model acquisition, exploiting the knowledge acquired by other users’s devices and web databases to bootstrap activity recognition.

At ETH Zürich we focus on the development of the crowd-sourcing approach to activity recognition. The University of Applied Sciences at Yverdon focuses on unsupervised hierarchical data clustering techniques. These two approaches are combined into a series of joint evaluations in a large scale deployement. Thus, a tight collaboration between the two institutes is foreseen.

=== Job description ===

We offer a PhD position within the framework of the 3 year long (2011-2014) Smart-Days project. In this position you will be responsible for one of the project’s work package. This work package comprises all elements required to achieve a robust crowd-sourced acquisition of human activity models. You will closely collaborate with the project’s partners throughout the duration of the project.

Your work environment will be multinational with frequent travels to the partner’s location.

Within this project, your research topics will include (but are not limited to):
* Activity and context recognition on mobile phones: Real-time streaming signal processing and machine learning on mobile phones or embedded systems
* Online adaptive machine learning: including subsets of supervised, semi-supervised, unsupervised techniques, as well as transfer learning and multitask learning
* Incentive design and capture of sporadic user feedback: methods allowing to capture user feedback are devised, allowing to maximize information gain and minimize user disturbance.
* Real-world deployment and evaluation: experiments during several weeks or months with a mobile platform deployed with an app store.
* Multimodal data fusion

Starting date: ASAP

=== Requirements ===

The candidate has a diploma, MSc, or equivalent in electrical engineering, micro-engineering, computer science or mathematics.
He has strong interests in mobile computing, machine learning/pattern recognition, signal processing, adaptive and learning systems, and in the combination of theoretical and experimental research.
Fluent spoken and written English is mandatory.

=== Contact and application ===

For further information about the Smart-Days project and your contribution within it, please contact Dr. Daniel Roggen (droggen(at)ife.ee.ethz.ch), or Prof. Andres Perez-Uribe (andres.perez-uribe(at)heig-vd.ch).

If you are interested and believe that you qualify, please send your application to Prof. Gerhard Tröster (troester(at)ife.ee.ethz.ch). Include:
* Curriculum Vitae with the names and contact details of at least 2 references
* a list of exams and grades obtained
* a cover letter explaining how your skills and research interests fit the project

For more information:
– http://www.wearable.ethz.ch/
– http://www.ife.ee.ethz.ch/openpositions/job_smartdays

July 8, 2011

PASCAL2

Open PhD Position @ ETH Zurich “Indoor Localization and Context Recognition for Patients with Parkinson’s Disease”