A cooperative coevolutionary approach dealing with the skull–face overlay uncertainty in forensic identification by craniofacial superimposition

Posted on October 12, 2011 by Community

Abstract  
Craniofacial superimposition is a forensic process where photographs or video shots of a missing person are compared with
the skull that is found. By projecting both photographs on top of each other (or, even better, matching a scanned three-dimensional
skull model against the face photo/video shot), the forensic anthropologist can try to establish whether that is the same
person. The whole process is influenced by inherent uncertainty mainly because two objects of different nature (a skull and
a face) are involved. In previous work, we categorized the different sources of uncertainty and introduced the use of imprecise
landmarks to tackle most of them. In this paper, we propose a novel approach, a cooperative coevolutionary algorithm, to deal
with the use of imprecise cephalometric landmarks in the skull–face overlay process, the main task in craniofacial superimposition.
Following this approach we are able to look for both the best projection parameters and the best landmark locations at the
same time. Coevolutionary skull–face overlay results are compared with our previous fuzzy-evolutionary automatic method. Six
skull–face overlay problem instances corresponding to three real-world cases solved by the Physical Anthropology Lab at the
University of Granada (Spain) are considered. Promising results have been achieved, dramatically reducing the run time while
improving the accuracy and robustness.

  • Content Type Journal Article
  • Category Focus
  • Pages 1-12
  • DOI 10.1007/s00500-011-0770-8
  • Authors
    • O. Ibáñez, European Centre for Soft Computing, 33600 Mieres, Asturias, Spain
    • O. Cordón, European Centre for Soft Computing, 33600 Mieres, Asturias, Spain
    • S. Damas, European Centre for Soft Computing, 33600 Mieres, Asturias, Spain
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New algorithms for finding approximate frequent item sets

Posted on October 10, 2011 by Community

Abstract  
In standard frequent item set mining a transaction supports an item set only if all items in the set are present. However,
in many cases this is too strict a requirement that can render it impossible to find certain relevant groups of items. By
relaxing the support definition, allowing for some items of a given set to be missing from a transaction, this drawback can
be amended. The resulting item sets have been called approximate, fault-tolerant or fuzzy item sets. In this paper we present
two new algorithms to find such item sets: the first is an extension of item set mining based on cover similarities and computes
and evaluates the subset size occurrence distribution with a scheme that is related to the Eclat algorithm. The second employs
a clustering-like approach, in which the distances are derived from the item covers with distance measures for sets or binary
vectors and which is initialized with a one-dimensional Sammon projection of the distance matrix. We demonstrate the benefits
of our algorithms by applying them to a concept detection task on the 2008/2009 Wikipedia Selection for schools and to the
neurobiological task of detecting neuron ensembles in (simulated) parallel spike trains.

  • Content Type Journal Article
  • Category Focus
  • Pages 1-15
  • DOI 10.1007/s00500-011-0776-2
  • Authors
    • Christian Borgelt, European Centre for Soft Computing, c/ Gonzalo Gutiérrez Quirós s/n, 33600 Mieres (Asturias), Spain
    • Christian Braune, European Centre for Soft Computing, c/ Gonzalo Gutiérrez Quirós s/n, 33600 Mieres (Asturias), Spain
    • Tobias Kötter, Department of Computer Science, University of Konstanz, Box 712, 78457 Constance, Germany
    • Sonja Grün, RIKEN Brain Science Institute, Wako-Shi, Saitama 351-0198, Japan
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Evolutionary computation as an artificial attacker: generating evasion attacks for detector vulnerability testing

Posted on October 8, 2011 by Community

Abstract  
Intrusion detection systems protect our infrastructures by monitoring for signs of intrusions. However, intrusion detection
systems are themselves susceptible to vulnerabilities, which the attackers take advantage of to evade detection. In particular,
we focus on evasion attacks in which the attacker aims to generate a stealthy attack that eliminates or minimizes the likelihood
of detection. Attackers achieve stealth by mimicking normal behaviour while achieving the attack goals, hence bypassing the
detector. Previous work focused on generating evasion attacks using the internal knowledge of the detectors, hence adopting
a ‘white-box’ access to the detector. On the other hand, we adopt a ‘black-box’ approach and propose an evolutionary attacker
based on Genetic Programming. The access of our ‘black-box’ approach is limited to the feedback of the detector such as anomaly
rates and delays. We compare our ‘black-box’ approach with various ‘white-box’ approaches to investigate its effectiveness.
In doing so, the impact of anomalies from the break-in stage of the attacks and the delays based on locality frame counts
are also discussed. This is particularly important if the performance comparison is to reflect the real capabilities of detectors.

  • Content Type Journal Article
  • Category Research Paper
  • Pages 1-24
  • DOI 10.1007/s12065-011-0065-0
  • Authors
    • Hilmi Güneş Kayacık, School of Computer Science, Carleton University, 1125 Colonel By Drive, Ottawa, ON K1S 5B6, Canada
    • A. Nur Zincir-Heywood, Faculty of Computer Science, Dalhousie University, 6050 University Avenue, Halifax, NS B3H 1W5, Canada
    • Malcolm I. Heywood, Faculty of Computer Science, Dalhousie University, 6050 University Avenue, Halifax, NS B3H 1W5, Canada
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Singular spectral analysis of ill-known signals and its application to predictive maintenance of windmills with SCADA records

Posted on October 7, 2011 by Community

Abstract  
A generalization of the singular spectral analysis (SSA) technique to ill-defined data is introduced in this paper. The proposed
algorithm achieves tight estimates of the energy of irregular or aperiodic oscillations from records of interval or fuzzy-valued
signals. Fuzzy signals are given a possibilistic interpretation as families of nested confidence intervals. In this context,
some types of Supervisory Control And Data Analysis (SCADA) records, where the minimum, mean and maximum values of the signal
between two scans are logged, are regarded as fuzzy constrains of the values of the sampled signal. The generalized SSA of
these records produces a set of interval-valued or fuzzy coefficients, that bound the spectral transform of the SCADA data.
Furthermore, these bounds are compared to the expected energy of AR(1) red noise, and the irrelevant components are discarded.
This comparison is accomplished using statistical tests for low quality data, that are in turn consistent with the possibilistic
interpretation of a fuzzy signal mentioned before. Generalized SSA has been applied to solve a real world problem, with SCADA
data taken from 40 turbines in a Spanish wind farm. It was found that certain oscillations in the pressure at the hydraulic
circuit of the tip brakes are correlated to long term damages in the windmill gear, showing that this new technique is useful
as a failure indicator in the predictive maintenance of windmills.

  • Content Type Journal Article
  • Category Focus
  • Pages 1-14
  • DOI 10.1007/s00500-011-0767-3
  • Authors
    • Luciano Sánchez, Computer Science Department, University of Oviedo, Campus de Viesques, 33071 Gijón, Asturias, Spain
    • Inés Couso, Facultad de Ciencias, Statistics Department, University of Oviedo, 33071 Oviedo, Asturias, Spain
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Guest editorial: special issue on “knowledge extraction from low quality data: theoretical, methodological and practical issues”

Posted on October 6, 2011 by Community

Guest editorial: special issue on “knowledge extraction from low quality data: theoretical, methodological and practical issues”

  • Content Type Journal Article
  • Category Editorial
  • Pages 1-2
  • DOI 10.1007/s00500-011-0765-5
  • Authors
    • Luciano Sánchez, Computer Science Department, University of Oviedo, Campus de Viesques, 33071 Gijón, Asturias, Spain
    • Inés Couso, Statistics Department, University of Oviedo, Facultad de Ciencias, 33071 Oviedo, Asturias, Spain
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Response to the review of "Variation-Aware Analog Structural Synthesis: A Computational Intelligence Approach"

Posted on October 4, 2011 by Community

Trent McConaghy, coauthor of Variation-Aware Analog Structural Synthesis: A Computational Intelligence Approach, wrote a response to the review of the book in Genetic Programming and Evolvable Machines and I have agreed to publish his response here. Trent’s letter follows:

In the December issue of Genetic Programming and Evolvable Machines (volume 12:4), John Rieffel gave a review of the book “Variation-Aware Analog Structural Synthesis: A Computational Intelligence Approach”, of which I was a co-author.  

We would like to express our thanks to John for a thoughtful review, covering the reliable and trustworthy approaches to perform industrially-oriented symbolic regression, robust optimization, and analog structural synthesis.  

We would like to clarify one point: while the review reports that the book ignores “simulators that cheat”, the book in fact dedicates substantial space on this issue, and how open-ended synthesis approaches are highly prone to it.  (See pp. 157-167, including the section “SPICE can lie”.)

The broader issue — trustworthy synthesis — is a broad challenge that the last half of the book addresses.  Trustworthy synthesis outputs circuits that a designer trusts enough to commit to silicon.  The book proposes to use hierarchical building blocks developed over the decades by expert designers, enabling synthesis to output circuits that are trustworthy by construction.

As the book discusses, the computational intelligence techniques presented generalize beyond analog CAD, to domains such as robotics, financial engineering, mechanical design, and more.  

 – Trent McConaghy, October 3, 2011

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Response to the review of "Variation-Aware Analog Structural Synthesis: A Computational Intelligence Approach"

Posted on October 4, 2011 by Community

Trent McConaghy, coauthor of Variation-Aware Analog Structural Synthesis: A Computational Intelligence Approach, wrote a response to the review of the book in Genetic Programming and Evolvable Machines and I have agreed to publish his response here. Trent’s letter follows:

In the December issue of Genetic Programming and Evolvable Machines (volume 12:4), John Rieffel gave a review of the book “Variation-Aware Analog Structural Synthesis: A Computational Intelligence Approach”, of which I was a co-author.  

We would like to express our thanks to John for a thoughtful review, covering the reliable and trustworthy approaches to perform industrially-oriented symbolic regression, robust optimization, and analog structural synthesis.  

We would like to clarify one point: while the review reports that the book ignores “simulators that cheat”, the book in fact dedicates substantial space on this issue, and how open-ended synthesis approaches are highly prone to it.  (See pp. 157-167, including the section “SPICE can lie”.)

The broader issue — trustworthy synthesis — is a broad challenge that the last half of the book addresses.  Trustworthy synthesis outputs circuits that a designer trusts enough to commit to silicon.  The book proposes to use hierarchical building blocks developed over the decades by expert designers, enabling synthesis to output circuits that are trustworthy by construction.

As the book discusses, the computational intelligence techniques presented generalize beyond analog CAD, to domains such as robotics, financial engineering, mechanical design, and more.  

 – Trent McConaghy, October 3, 2011

Comments Off

Response to the review of "Variation-Aware Analog Structural Synthesis: A Computational Intelligence Approach"

Posted on October 4, 2011 by Community

Trent McConaghy, coauthor of Variation-Aware Analog Structural Synthesis: A Computational Intelligence Approach, wrote a response to the review of the book in Genetic Programming and Evolvable Machines and I have agreed to publish his response here. Trent’s letter follows:

In the December issue of Genetic Programming and Evolvable Machines (volume 12:4), John Rieffel gave a review of the book “Variation-Aware Analog Structural Synthesis: A Computational Intelligence Approach”, of which I was a co-author.  

We would like to express our thanks to John for a thoughtful review, covering the reliable and trustworthy approaches to perform industrially-oriented symbolic regression, robust optimization, and analog structural synthesis.  

We would like to clarify one point: while the review reports that the book ignores “simulators that cheat”, the book in fact dedicates substantial space on this issue, and how open-ended synthesis approaches are highly prone to it.  (See pp. 157-167, including the section “SPICE can lie”.)

The broader issue — trustworthy synthesis — is a broad challenge that the last half of the book addresses.  Trustworthy synthesis outputs circuits that a designer trusts enough to commit to silicon.  The book proposes to use hierarchical building blocks developed over the decades by expert designers, enabling synthesis to output circuits that are trustworthy by construction.

As the book discusses, the computational intelligence techniques presented generalize beyond analog CAD, to domains such as robotics, financial engineering, mechanical design, and more.  

 – Trent McConaghy, October 3, 2011

Comments Off

Response to the review of "Variation-Aware Analog Structural Synthesis: A Computational Intelligence Approach"

Posted on October 4, 2011 by Community

Trent McConaghy, coauthor of Variation-Aware Analog Structural Synthesis: A Computational Intelligence Approach, wrote a response to the review of the book in Genetic Programming and Evolvable Machines and I have agreed to publish his response here. Trent’s letter follows:

In the December issue of Genetic Programming and Evolvable Machines (volume 12:4), John Rieffel gave a review of the book “Variation-Aware Analog Structural Synthesis: A Computational Intelligence Approach”, of which I was a co-author.  

We would like to express our thanks to John for a thoughtful review, covering the reliable and trustworthy approaches to perform industrially-oriented symbolic regression, robust optimization, and analog structural synthesis.  

We would like to clarify one point: while the review reports that the book ignores “simulators that cheat”, the book in fact dedicates substantial space on this issue, and how open-ended synthesis approaches are highly prone to it.  (See pp. 157-167, including the section “SPICE can lie”.)

The broader issue — trustworthy synthesis — is a broad challenge that the last half of the book addresses.  Trustworthy synthesis outputs circuits that a designer trusts enough to commit to silicon.  The book proposes to use hierarchical building blocks developed over the decades by expert designers, enabling synthesis to output circuits that are trustworthy by construction.

As the book discusses, the computational intelligence techniques presented generalize beyond analog CAD, to domains such as robotics, financial engineering, mechanical design, and more.  

 – Trent McConaghy, October 3, 2011

Comments Off

Response to the review of "Variation-Aware Analog Structural Synthesis: A Computational Intelligence Approach"

Posted on October 4, 2011 by Community

Trent McConaghy, coauthor of Variation-Aware Analog Structural Synthesis: A Computational Intelligence Approach, wrote a response to the review of the book in Genetic Programming and Evolvable Machines and I have agreed to publish his response here. Trent’s letter follows:

In the December issue of Genetic Programming and Evolvable Machines (volume 12:4), John Rieffel gave a review of the book “Variation-Aware Analog Structural Synthesis: A Computational Intelligence Approach”, of which I was a co-author.  

We would like to express our thanks to John for a thoughtful review, covering the reliable and trustworthy approaches to perform industrially-oriented symbolic regression, robust optimization, and analog structural synthesis.  

We would like to clarify one point: while the review reports that the book ignores “simulators that cheat”, the book in fact dedicates substantial space on this issue, and how open-ended synthesis approaches are highly prone to it.  (See pp. 157-167, including the section “SPICE can lie”.)

The broader issue — trustworthy synthesis — is a broad challenge that the last half of the book addresses.  Trustworthy synthesis outputs circuits that a designer trusts enough to commit to silicon.  The book proposes to use hierarchical building blocks developed over the decades by expert designers, enabling synthesis to output circuits that are trustworthy by construction.

As the book discusses, the computational intelligence techniques presented generalize beyond analog CAD, to domains such as robotics, financial engineering, mechanical design, and more.  

 – Trent McConaghy, October 3, 2011

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