pwc-archive/papers-with-abstracts huggingface dataset:
Gabor-like Image Filtering using a Neural Microcircuit
Authors: C. Mayr, A. Heittmann, R. Schüffny
Date: 2014-08-08 00:00:00

In this letter, we present an implementation of a neural microcircuit for
image processing employing Hebbian-adaptive learning. The neuronal circuit
utilizes only excitatory synapses to correlate action potentials, extracting
the uncorrelated ones, which contain significant image information. This
circuit is capable of approximating Gabor-like image filtering and other image
processing functions
A model of grassroots changes in linguistic systems
Authors: Janet B. Pierrehumbert, Forrest Stonedahl, Robert Daland
Date: 2014-08-08 00:00:00

Linguistic norms emerge in human communities because people imitate each
other. A shared linguistic system provides people with the benefits of shared
knowledge and coordinated planning. Once norms are in place, why would they
ever change? This question, echoing broad questions in the theory of social
dynamics, has particular force in relation to language. By definition, an
innovator is in the minority when the innovation first occurs. In some areas of
social dynamics, important minorities can strongly influence the majority
through their power, fame, or use of broadcast media. But most linguistic
changes are grassroots developments that originate with ordinary people. Here,
we develop a novel model of communicative behavior in communities, and identify
a mechanism for arbitrary innovations by ordinary people to have a good chance
of being widely adopted.
  To imitate each other, people must form a mental representation of what other
people do. Each time they speak, they must also decide which form to produce
themselves. We introduce a new decision function that enables us to smoothly
explore the space between two types of behavior: probability matching (matching
the probabilities of incoming experience) and regularization (producing some
forms disproportionately often). Using Monte Carlo methods, we explore the
interactions amongst the degree of regularization, the distribution of biases
in a network, and the network position of the innovator. We identify two
regimes for the widespread adoption of arbritrary innovations, viewed as
informational cascades in the network. With moderate regularization of
experienced input, average people (not well-connected people) are the most
likely source of successful innovations. Our results shed light on a major
outstanding puzzle in the theory of language change. The framework also holds
promise for understanding the dynamics of other social norms.
Neighborhood Rank Order Coding for Robust Texture Analysis and Feature Extraction
Authors: C. Mayr, R. Schüffny
Date: 2014-08-08 00:00:00
Tasks: Texture Classification

Research into the visual cortex and general neural information processing has
led to various attempts to integrate pulse computation schemes in image
analysis systems. Of interest is especially the robustness of representing an
analogue signal in the phase or duration of a pulsed, quasi-digital signal, as
well as the possibility of direct digital interaction, i.e. computation, among
these signals. Such a computation can also achieve information compaction for
subsequent processing stages. By using a pulse order encoding scheme motivated
by dendritic pulse interaction, we will show that a powerful low-level feature
and texture extraction operator, called Pulsed Local Orientation Coding (PLOC),
can be implemented. Feature extraction results are being presented, and a
possible VLSI implementation is detailed.
Microtask crowdsourcing for disease mention annotation in PubMed abstracts
Authors: Benjamin M. Good, Max Nanis, Andrew I. Su
Date: 2014-08-08 00:00:00
Tasks: Benchmarking

Identifying concepts and relationships in biomedical text enables knowledge
to be applied in computational analyses. Many biological natural language
process (BioNLP) projects attempt to address this challenge, but the state of
the art in BioNLP still leaves much room for improvement. Progress in BioNLP
research depends on large, annotated corpora for evaluating information
extraction systems and training machine learning models. Traditionally, such
corpora are created by small numbers of expert annotators often working over
extended periods of time. Recent studies have shown that workers on microtask
crowdsourcing platforms such as Amazon's Mechanical Turk (AMT) can, in
aggregate, generate high-quality annotations of biomedical text. Here, we
investigated the use of the AMT in capturing disease mentions in PubMed
abstracts. We used the NCBI Disease corpus as a gold standard for refining and
benchmarking our crowdsourcing protocol. After several iterations, we arrived
at a protocol that reproduced the annotations of the 593 documents in the
training set of this gold standard with an overall F measure of 0.872
(precision 0.862, recall 0.883). The output can also be tuned to optimize for
precision (max = 0.984 when recall = 0.269) or recall (max = 0.980 when
precision = 0.436). Each document was examined by 15 workers, and their
annotations were merged based on a simple voting method. In total 145 workers
combined to complete all 593 documents in the span of 1 week at a cost of $.06
per abstract per worker. The quality of the annotations, as judged with the F
measure, increases with the number of workers assigned to each task such that
the system can be tuned to balance cost against quality. These results
demonstrate that microtask crowdsourcing can be a valuable tool for generating
well-annotated corpora in BioNLP.
Using Learned Predictions as Feedback to Improve Control and Communication with an Artificial Limb: Preliminary Findings
Authors: Adam S. R. Parker, Ann L. Edwards, Patrick M. Pilarski
Date: 2014-08-08 00:00:00

Many people suffer from the loss of a limb. Learning to get by without an arm
or hand can be very challenging, and existing prostheses do not yet fulfil the
needs of individuals with amputations. One promising solution is to provide
greater communication between a prosthesis and its user. Towards this end, we
present a simple machine learning interface to supplement the control of a
robotic limb with feedback to the user about what the limb will be experiencing
in the near future. A real-time prediction learner was implemented to predict
impact-related electrical load experienced by a robot limb; the learning
system's predictions were then communicated to the device's user to aid in
their interactions with a workspace. We tested this system with five
able-bodied subjects. Each subject manipulated the robot arm while receiving
different forms of vibrotactile feedback regarding the arm's contact with its
workspace. Our trials showed that communicable predictions could be learned
quickly during human control of the robot arm. Using these predictions as a
basis for feedback led to a statistically significant improvement in task
performance when compared to purely reactive feedback from the device. Our
study therefore contributes initial evidence that prediction learning and
machine intelligence can benefit not just control, but also feedback from an
artificial limb. We expect that a greater level of acceptance and ownership can
be achieved if the prosthesis itself takes an active role in transmitting
learned knowledge about its state and its situation of use.
Exploring the evolution of a trade-off between vigilance and foraging in group-living organisms
Authors: Randal S. Olson, Patrick B. Haley, Fred C. Dyer, Christoph Adami
Date: 2014-08-08 00:00:00

Despite the fact that grouping behavior has been actively studied for over a
century, the relative importance of the numerous proposed fitness benefits of
grouping remain unclear. We use a digital model of evolving prey under
simulated predation to directly explore the evolution of gregarious foraging
behavior according to one such benefit, the "many eyes" hypothesis. According
to this hypothesis, collective vigilance allows prey in large groups to detect
predators more efficiently by making alarm signals or behavioral cues to each
other, thereby allowing individuals within the group to spend more time
foraging. Here, we find that collective vigilance is sufficient to select for
gregarious foraging behavior as long there is not a direct cost for grouping
(e.g., competition for limited food resources), even when controlling for
confounding factors such as the dilution effect. Further, we explore the role
of the genetic relatedness and reproductive strategy of the prey, and find that
highly related groups of prey with a semelparous reproductive strategy are the
most likely to evolve gregarious foraging behavior mediated by the benefit of
vigilance. These findings, combined with earlier studies with evolving digital
organisms, further sharpen our understanding of the factors favoring grouping
behavior.
Rate Prediction and Selection in LTE systems using Modified Source Encoding Techniques
Authors: K. P. Saishankar, Sheetal Kalyani, K. Narendran
Date: 2014-03-06 00:00:00
Tasks: Prediction

In current wireless systems, the base-Station (eNodeB) tries to serve its
user-equipment (UE) at the highest possible rate that the UE can reliably
decode. The eNodeB obtains this rate information as a quantized feedback from
the UE at time n and uses this, for rate selection till the next feedback is
received at time n + {\delta}. The feedback received at n can become outdated
before n + {\delta}, because of a) Doppler fading, and b) Change in the set of
active interferers for a UE. Therefore rate prediction becomes essential.
Since, the rates belong to a discrete set, we propose a discrete sequence
prediction approach, wherein, frequency trees for the discrete sequences are
built using source encoding algorithms like Prediction by Partial Match (PPM).
Finding the optimal depth of the frequency tree used for prediction is cast as
a model order selection problem. The rate sequence complexity is analysed to
provide an upper bound on model order. Information-theoretic criteria are then
used to solve the model order problem. Finally, two prediction algorithms are
proposed, using the PPM with optimal model order and system level simulations
demonstrate the improvement in packet loss and throughput due to these
algorithms.
Origin of the computational hardness for learning with binary synapses
Authors: Haiping Huang, Yoshiyuki Kabashima
Date: 2014-08-08 00:00:00

Supervised learning in a binary perceptron is able to classify an extensive
number of random patterns by a proper assignment of binary synaptic weights.
However, to find such assignments in practice, is quite a nontrivial task. The
relation between the weight space structure and the algorithmic hardness has
not yet been fully understood. To this end, we analytically derive the
Franz-Parisi potential for the binary preceptron problem, by starting from an
equilibrium solution of weights and exploring the weight space structure around
it. Our result reveals the geometrical organization of the weight
space\textemdash the weight space is composed of isolated solutions, rather
than clusters of exponentially many close-by solutions. The point-like clusters
far apart from each other in the weight space explain the previously observed
glassy behavior of stochastic local search heuristics.
A compact formula for the derivative of a 3-D rotation in exponential coordinates
Authors: Guillermo Gallego, Anthony Yezzi
Date: 2013-12-03 00:00:00

We present a compact formula for the derivative of a 3-D rotation matrix with
respect to its exponential coordinates. A geometric interpretation of the
resulting expression is provided, as well as its agreement with other
less-compact but better-known formulas. To the best of our knowledge, this
simpler formula does not appear anywhere in the literature. We hope by
providing this more compact expression to alleviate the common pressure to
reluctantly resort to alternative representations in various computational
applications simply as a means to avoid the complexity of differential analysis
in exponential coordinates.
Beyond description. Comment on "Approaching human language with complex networks" by Cong & Liu
Authors: Ramon Ferrer-i-Cancho
Date: 2014-08-08 00:00:00

Comment on "Approaching human language with complex networks" by Cong & Liu
Real-Time and Robust Method for Hand Gesture Recognition System Based on Cross-Correlation Coefficient
Authors: Reza Azad, Babak Azad, Iman Tavakoli Kazerooni
Date: 2014-08-08 00:00:00
Tasks: Gesture Recognition, Hand Gesture Recognition, Hand-Gesture Recognition, Image Segmentation, Semantic Segmentation, Sign Language Recognition

Hand gesture recognition possesses extensive applications in virtual reality, sign language recognition, and computer games. The direct interface of hand gestures provides us a new way for communicating with the virtual environment. In this paper a novel and real-time approach for hand gesture recognition system is presented. In the suggested method, first, the hand gesture is extracted from the main image by the image segmentation and morphological operation and then is sent to feature extraction stage. In feature extraction stage the Cross-correlation coefficient is applied on the gesture to recognize it. In the result part, the proposed approach is applied on American Sign Language (ASL) database and the accuracy rate obtained 98.34%.
Low-rank SIFT: An Affine Invariant Feature for Place Recognition
Authors: Chao Yang, Shengnan Caih, Jingdong Wang, Long Quan
Date: 2014-08-07 00:00:00
Tasks: feature selection, parameter estimation, Translation

In this paper, we present a novel affine-invariant feature based on SIFT,
leveraging the regular appearance of man-made objects. The feature achieves
full affine invariance without needing to simulate over affine parameter space.
Low-rank SIFT, as we name the feature, is based on our observation that local
tilt, which are caused by changes of camera axis orientation, could be
normalized by converting local patches to standard low-rank forms. Rotation,
translation and scaling invariance could be achieved in ways similar to SIFT.
As an extension of SIFT, our method seeks to add prior to solve the ill-posed
affine parameter estimation problem and normalizes them directly, and is
applicable to objects with regular structures. Furthermore, owing to recent
breakthrough in convex optimization, such parameter could be computed
efficiently. We will demonstrate its effectiveness in place recognition as our
major application. As extra contributions, we also describe our pipeline of
constructing geotagged building database from the ground up, as well as an
efficient scheme for automatic feature selection.
Tracking Individual Targets in High Density Crowd Scenes Analysis of a Video Recording in Hajj 2009
Authors: Mohamed H. Dridi
Date: 2014-07-08 00:00:00
Tasks: Motion Estimation, Optical Flow Estimation

In this paper we present a number of methods (manual, semi-automatic and
automatic) for tracking individual targets in high density crowd scenes where
thousand of people are gathered. The necessary data about the motion of
individuals and a lot of other physical information can be extracted from
consecutive image sequences in different ways, including optical flow and block
motion estimation. One of the famous methods for tracking moving objects is the
block matching method. This way to estimate subject motion requires the
specification of a comparison window which determines the scale of the
estimate. In this work we present a real-time method for pedestrian recognition
and tracking in sequences of high resolution images obtained by a stationary
(high definition) camera located in different places on the Haram mosque in
Mecca. The objective is to estimate pedestrian velocities as a function of the
local density.The resulting data of tracking moving pedestrians based on video
sequences are presented in the following section. Through the evaluated system
the spatio-temporal coordinates of each pedestrian during the Tawaf ritual are
established. The pilgrim velocities as function of the local densities in the
Mataf area (Haram Mosque Mecca) are illustrated and very precisely documented.
Real-Time Human-Computer Interaction Based on Face and Hand Gesture Recognition
Authors: Reza Azad, Babak Azad, Nabil Belhaj Khalifa, Shahram Jamali
Date: 2014-08-07 00:00:00
Tasks: Face Recognition, Gesture Recognition, Hand Gesture Recognition, Hand-Gesture Recognition

At the present time, hand gestures recognition system could be used as a more expected and useable approach for human computer interaction. Automatic hand gesture recognition system provides us a new tactic for interactive with the virtual environment. In this paper, a face and hand gesture recognition system which is able to control computer media player is offered. Hand gesture and human face are the key element to interact with the smart system. We used the face recognition scheme for viewer verification and the hand gesture recognition in mechanism of computer media player, for instance, volume down/up, next music and etc. In the proposed technique, first, the hand gesture and face location is extracted from the main image by combination of skin and cascade detector and then is sent to recognition stage. In recognition stage, first, the threshold condition is inspected then the extracted face and gesture will be recognized. In the result stage, the proposed technique is applied on the video dataset and the high precision ratio acquired. Additional the recommended hand gesture recognition method is applied on static American Sign Language (ASL) database and the correctness rate achieved nearby 99.40%. also the planned method could be used in gesture based computer games and virtual reality.
Face Detection Using Radial Basis Functions Neural Networks With Fixed Spread
Authors: K. A. A. Aziz, S. S. Abdullah
Date: 2014-08-07 00:00:00
Tasks: Face Detection, Face Recognition

This paper presented a face detection system using Radial Basis Function Neural Networks With Fixed Spread Value. Face detection is the first step in face recognition system. The purpose is to localize and extract the face region from the background that will be fed into the face recognition system for identification. General preprocessing approach was used for normalizing the image and Radial Basis Function (RBF) Neural Network was used to distinguish between face and non-face. RBF Neural Networks offer several advantages compared to other neural network architecture such as they can be trained using fast two stages training algorithm and the network possesses the property of best approximation. The output of the network can be optimized by setting suitable value of center and spread of the RBF. In this paper, fixed spread value will be used. The Radial Basis Function Neural Network (RBFNN) used to distinguish faces and non-faces and the evaluation of the system will be the performance of detection, False Acceptance Rate (FAR), False Rejection Rate (FRR) and the discriminative properties.
Renewal Strings for Cleaning Astronomical Databases
Authors: Amos J. Storkey, Nigel C. Hambly, Christopher K. I. Williams, Robert G. Mann
Date: 2014-08-07 00:00:00

Large astronomical databases obtained from sky surveys such as the
SuperCOSMOS Sky Surveys (SSS) invariably suffer from a small number of spurious
records coming from artefactual effects of the telescope, satellites and junk
objects in orbit around earth and physical defects on the photographic plate or
CCD. Though relatively small in number these spurious records present a
significant problem in many situations where they can become a large proportion
of the records potentially of interest to a given astronomer. In this paper we
focus on the four most common causes of unwanted records in the SSS: satellite
or aeroplane tracks, scratches fibres and other linear phenomena introduced to
the plate, circular halos around bright stars due to internal reflections
within the telescope and diffraction spikes near to bright stars. Accurate and
robust techniques are needed for locating and flagging such spurious objects.
We have developed renewal strings, a probabilistic technique combining the
Hough transform, renewal processes and hidden Markov models which have proven
highly effective in this context. The methods are applied to the SSS data to
develop a dataset of spurious object detections, along with confidence
measures, which can allow this unwanted data to be removed from consideration.
These methods are general and can be adapted to any future astronomical survey
data.
Updating with incomplete observations
Authors: Gert de Cooman, Marco Zaffalon
Date: 2014-08-07 00:00:00

Currently, there is renewed interest in the problem, raised by Shafer in
1985, of updating probabilities when observations are incomplete (or
set-valued). This is a fundamental problem, and of particular interest for
Bayesian networks. Recently, Grunwald and Halpern have shown that commonly used
updating strategies fail here, except under very special assumptions. We
propose a new rule for updating probabilities with incomplete observations. Our
approach is deliberately conservative: we make no or weak assumptions about the
so-called incompleteness mechanism that produces incomplete observations. We
model our ignorance about this mechanism by a vacuous lower prevision, a tool
from the theory of imprecise probabilities, and we derive a new updating rule
using coherence arguments. In general, our rule produces lower posterior
probabilities, as well as partially determinate decisions. This is a logical
consequence of the ignorance about the incompleteness mechanism. We show how
the new rule can properly address the apparent paradox in the 'Monty Hall'
puzzle. In addition, we apply it to the classification of new evidence in
Bayesian networks constructed using expert knowledge. We provide an exact
algorithm for this task with linear-time complexity, also for multiply
connected nets.
Robust Feature Selection by Mutual Information Distributions
Authors: Marco Zaffalon, Marcus Hutter
Date: 2014-08-07 00:00:00
Tasks: Descriptive, feature selection, Incremental Learning

Mutual information is widely used in artificial intelligence, in a
descriptive way, to measure the stochastic dependence of discrete random
variables. In order to address questions such as the reliability of the
empirical value, one must consider sample-to-population inferential approaches.
This paper deals with the distribution of mutual information, as obtained in a
Bayesian framework by a second-order Dirichlet prior distribution. The exact
analytical expression for the mean and an analytical approximation of the
variance are reported. Asymptotic approximations of the distribution are
proposed. The results are applied to the problem of selecting features for
incremental learning and classification of the naive Bayes classifier. A fast,
newly defined method is shown to outperform the traditional approach based on
empirical mutual information on a number of real data sets. Finally, a
theoretical development is reported that allows one to efficiently extend the
above methods to incomplete samples in an easy and effective way.
A Logic for Reasoning about Upper Probabilities
Authors: Joseph Y. Halpern, Riccardo Pucella
Date: 2014-08-07 00:00:00

We present a propositional logic to reason about the uncertainty of events,
where the uncertainty is modeled by a set of probability measures assigning an
interval of probability to each event. We give a sound and complete
axiomatization for the logic, and show that the satisfiability problem is
NP-complete, no harder than satisfiability for propositional logic.