pwc-archive/papers-with-abstracts huggingface dataset:
Pixels to Voxels: Modeling Visual Representation in the Human Brain
Authors: Pulkit Agrawal, Dustin Stansbury, Jitendra Malik, Jack L. Gallant
Date: 2014-07-18 00:00:00
Tasks: BIG-bench Machine Learning, Object Recognition

The human brain is adept at solving difficult high-level visual processing
problems such as image interpretation and object recognition in natural scenes.
Over the past few years neuroscientists have made remarkable progress in
understanding how the human brain represents categories of objects and actions
in natural scenes. However, all current models of high-level human vision
operate on hand annotated images in which the objects and actions have been
assigned semantic tags by a human operator. No current models can account for
high-level visual function directly in terms of low-level visual input (i.e.,
pixels). To overcome this fundamental limitation we sought to develop a new
class of models that can predict human brain activity directly from low-level
visual input (i.e., pixels). We explored two classes of models drawn from
computer vision and machine learning. The first class of models was based on
Fisher Vectors (FV) and the second was based on Convolutional Neural Networks
(ConvNets). We find that both classes of models accurately predict brain
activity in high-level visual areas, directly from pixels and without the need
for any semantic tags or hand annotation of images. This is the first time that
such a mapping has been obtained. The fit models provide a new platform for
exploring the functional principles of human vision, and they show that modern
methods of computer vision and machine learning provide important tools for
characterizing brain function.
Local Backbones
Authors: Ronald de Haan, Iyad Kanj, Stefan Szeider
Date: 2013-04-19 00:00:00

A backbone of a propositional CNF formula is a variable whose truth value is
the same in every truth assignment that satisfies the formula. The notion of
backbones for CNF formulas has been studied in various contexts. In this paper,
we introduce local variants of backbones, and study the computational
complexity of detecting them. In particular, we consider k-backbones, which are
backbones for sub-formulas consisting of at most k clauses, and iterative
k-backbones, which are backbones that result after repeated instantiations of
k-backbones. We determine the parameterized complexity of deciding whether a
variable is a k-backbone or an iterative k-backbone for various restricted
formula classes, including Horn, definite Horn, and Krom. We also present some
first empirical results regarding backbones for CNF-Satisfiability (SAT). The
empirical results we obtain show that a large fraction of the backbones of
structured SAT instances are local, in contrast to random instances, which
appear to have few local backbones.
Bayesian Nonparametric Crowdsourcing
Authors: Pablo G. Moreno, Yee Whye Teh, Fernando Perez-Cruz, Antonio Artés-Rodríguez
Date: 2014-07-18 00:00:00
Tasks: Active Learning

Crowdsourcing has been proven to be an effective and efficient tool to
annotate large datasets. User annotations are often noisy, so methods to
combine the annotations to produce reliable estimates of the ground truth are
necessary. We claim that considering the existence of clusters of users in this
combination step can improve the performance. This is especially important in
early stages of crowdsourcing implementations, where the number of annotations
is low. At this stage there is not enough information to accurately estimate
the bias introduced by each annotator separately, so we have to resort to
models that consider the statistical links among them. In addition, finding
these clusters is interesting in itself as knowing the behavior of the pool of
annotators allows implementing efficient active learning strategies. Based on
this, we propose in this paper two new fully unsupervised models based on a
Chinese Restaurant Process (CRP) prior and a hierarchical structure that allows
inferring these groups jointly with the ground truth and the properties of the
users. Efficient inference algorithms based on Gibbs sampling with auxiliary
variables are proposed. Finally, we perform experiments, both on synthetic and
real databases, to show the advantages of our models over state-of-the-art
algorithms.
Motor Learning Mechanism on the Neuron Scale
Authors: Peilei Liu, Ting Wang
Date: 2014-07-18 00:00:00

Based on existing data, we wish to put forward a biological model of motor
system on the neuron scale. Then we indicate its implications in statistics and
learning. Specifically, neuron firing frequency and synaptic strength are
probability estimates in essence. And the lateral inhibition also has
statistical implications. From the standpoint of learning, dendritic
competition through retrograde messengers is the foundation of conditional
reflex and grandmother cell coding. And they are the kernel mechanisms of motor
learning and sensory motor integration respectively. Finally, we compare motor
system with sensory system. In short, we would like to bridge the gap between
molecule evidences and computational models.
Deep Metric Learning for Practical Person Re-Identification
Authors: Dong Yi, Zhen Lei, Stan Z. Li
Date: 2014-07-18 00:00:00
Tasks: Metric Learning, Person Re-Identification

Various hand-crafted features and metric learning methods prevail in the
field of person re-identification. Compared to these methods, this paper
proposes a more general way that can learn a similarity metric from image
pixels directly. By using a "siamese" deep neural network, the proposed method
can jointly learn the color feature, texture feature and metric in a unified
framework. The network has a symmetry structure with two sub-networks which are
connected by Cosine function. To deal with the big variations of person images,
binomial deviance is used to evaluate the cost between similarities and labels,
which is proved to be robust to outliers.
  Compared to existing researches, a more practical setting is studied in the
experiments that is training and test on different datasets (cross dataset
person re-identification). Both in "intra dataset" and "cross dataset"
settings, the superiorities of the proposed method are illustrated on VIPeR and
PRID.
Hand Pointing Detection Using Live Histogram Template of Forehead Skin
Authors: Ghassem Tofighi, Nasser Ali Afarin, Kamraan Raahemifar, Anastasios N. Venetsanopoulos
Date: 2014-07-18 00:00:00

Hand pointing detection has multiple applications in many fields such as
virtual reality and control devices in smart homes. In this paper, we proposed
a novel approach to detect pointing vector in 2D space of a room. After
background subtraction, face and forehead is detected. In the second step,
forehead skin H-S plane histograms in HSV space is calculated. By using these
histogram templates of users skin, and back projection method, skin areas are
detected. The contours of hand are extracted using Freeman chain code
algorithm. Next step is finding fingertips. Points in hand contour which are
candidates for the fingertip can be found in convex defects of convex hull and
contour. We introduced a novel method for finding the fingertip based on the
special points on the contour and their relationships. Our approach detects
hand-pointing vectors in live video from a common webcam with 94%TP and 85%TN.
Classification of Passes in Football Matches using Spatiotemporal Data
Authors: Michael Horton, Joachim Gudmundsson, Sanjay Chawla, Joël Estephan
Date: 2014-07-18 00:00:00
Tasks: Classification, Game of Football, General Classification, Pass Classification

A knowledgeable observer of a game of football (soccer) can make a subjective
evaluation of the quality of passes made between players during the game. We
investigate the problem of producing an automated system to make the same
evaluation of passes. We present a model that constructs numerical predictor
variables from spatiotemporal match data using feature functions based on
methods from computational geometry, and then learns a classification function
from labelled examples of the predictor variables. Furthermore, the learned
classifiers are analysed to determine if there is a relationship between the
complexity of the algorithm that computed the predictor variable and the
importance of the variable to the classifier. Experimental results show that we
are able to produce a classifier with 85.8% accuracy on classifying passes as
Good, OK or Bad, and that the predictor variables computed using complex
methods from computational geometry are of moderate importance to the learned
classifiers. Finally, we show that the inter-rater agreement on pass
classification between the machine classifier and a human observer is of
similar magnitude to the agreement between two observers.
Affine Subspace Representation for Feature Description
Authors: Zhenhua Wang, Bin Fan, Fuchao Wu
Date: 2014-07-18 00:00:00

This paper proposes a novel Affine Subspace Representation (ASR) descriptor
to deal with affine distortions induced by viewpoint changes. Unlike the
traditional local descriptors such as SIFT, ASR inherently encodes local
information of multi-view patches, making it robust to affine distortions while
maintaining a high discriminative ability. To this end, PCA is used to
represent affine-warped patches as PCA-patch vectors for its compactness and
efficiency. Then according to the subspace assumption, which implies that the
PCA-patch vectors of various affine-warped patches of the same keypoint can be
represented by a low-dimensional linear subspace, the ASR descriptor is
obtained by using a simple subspace-to-point mapping. Such a linear subspace
representation could accurately capture the underlying information of a
keypoint (local structure) under multiple views without sacrificing its
distinctiveness. To accelerate the computation of ASR descriptor, a fast
approximate algorithm is proposed by moving the most computational part (ie,
warp patch under various affine transformations) to an offline training stage.
Experimental results show that ASR is not only better than the state-of-the-art
descriptors under various image transformations, but also performs well without
a dedicated affine invariant detector when dealing with viewpoint changes.
A Comparative Study of Meta-heuristic Algorithms for Solving Quadratic Assignment Problem
Authors: Gamal Abd El-Nasser A. Said, Abeer M. Mahmoud, El-Sayed M. El-Horbaty
Date: 2014-07-18 00:00:00
Tasks: Combinatorial Optimization

Quadratic Assignment Problem (QAP) is an NP-hard combinatorial optimization
problem, therefore, solving the QAP requires applying one or more of the
meta-heuristic algorithms. This paper presents a comparative study between
Meta-heuristic algorithms: Genetic Algorithm, Tabu Search, and Simulated
annealing for solving a real-life (QAP) and analyze their performance in terms
of both runtime efficiency and solution quality. The results show that Genetic
Algorithm has a better solution quality while Tabu Search has a faster
execution time in comparison with other Meta-heuristic algorithms for solving
QAP.
An landcover fuzzy logic classification by maximumlikelihood
Authors: T. Sarath, G. Nagalakshmi
Date: 2014-07-17 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Remote Sensing Image Classification

In present days remote sensing is most used application in many sectors. This
remote sensing uses different images like multispectral, hyper spectral or
ultra spectral. The remote sensing image classification is one of the
significant method to classify image. In this state we classify the maximum
likelihood classification with fuzzy logic. In this we experimenting fuzzy
logic like spatial, spectral texture methods in that different sub methods to
be used for image classification.
Initial Comparison of Linguistic Networks Measures for Parallel Texts
Authors: Kristina Ban, Ana Meštrović, Sanda Martinčić-Ipšić
Date: 2014-05-08 00:00:00
Tasks: Clustering

This paper presents preliminary results of Croatian syllable networks
analysis. Syllable network is a network in which nodes are syllables and links
between them are constructed according to their connections within words. In
this paper we analyze networks of syllables generated from texts collected from
the Croatian Wikipedia and Blogs. As a main tool we use complex network
analysis methods which provide mechanisms that can reveal new patterns in a
language structure. We aim to show that syllable networks have much higher
clustering coefficient in comparison to Erd\"os-Renyi random networks. The
results indicate that Croatian syllable networks exhibit certain properties of
a small world networks. Furthermore, we compared Croatian syllable networks
with Portuguese and Chinese syllable networks and we showed that they have
similar properties.
A preliminary study of Croatian Language Syllable Networks
Authors: Kristina Ban, Ivan Ivakić, Ana Meštrović
Date: 2014-05-16 00:00:00
Tasks: Clustering

This paper presents preliminary results of Croatian syllable networks
analysis. Syllable network is a network in which nodes are syllables and links
between them are constructed according to their connections within words. In
this paper we analyze networks of syllables generated from texts collected from
the Croatian Wikipedia and Blogs. As a main tool we use complex network
analysis methods which provide mechanisms that can reveal new patterns in a
language structure. We aim to show that syllable networks have much higher
clustering coefficient in comparison to Erd\"os-Renyi random networks. The
results indicate that Croatian syllable networks exhibit certain properties of
a small world networks. Furthermore, we compared Croatian syllable networks
with Portuguese and Chinese syllable networks and we showed that they have
similar properties.
Comparison of the language networks from literature and blogs
Authors: Sabina Šišović, Sanda Martinčić-Ipšić, Ana Meštrović
Date: 2014-05-12 00:00:00
Tasks: Clustering, Sentence

In this paper we present the comparison of the linguistic networks from
literature and blog texts. The linguistic networks are constructed from texts
as directed and weighted co-occurrence networks of words. Words are nodes and
links are established between two nodes if they are directly co-occurring
within the sentence. The comparison of the networks structure is performed at
global level (network) in terms of: average node degree, average shortest path
length, diameter, clustering coefficient, density and number of components.
Furthermore, we perform analysis on the local level (node) by comparing the
rank plots of in and out degree, strength and selectivity. The
selectivity-based results point out that there are differences between the
structure of the networks constructed from literature and blogs.
Toward Selectivity Based Keyword Extraction for Croatian News
Authors: Slobodan Beliga, Ana Meštrović, Sanda Martinčić-Ipšić
Date: 2014-07-17 00:00:00
Tasks: Keyword Extraction

Preliminary report on network based keyword extraction for Croatian is an
unsupervised method for keyword extraction from the complex network. We build
our approach with a new network measure the node selectivity, motivated by the
research of the graph based centrality approaches. The node selectivity is
defined as the average weight distribution on the links of the single node. We
extract nodes (keyword candidates) based on the selectivity value. Furthermore,
we expand extracted nodes to word-tuples ranked with the highest in/out
selectivity values. Selectivity based extraction does not require linguistic
knowledge while it is purely derived from statistical and structural
information en-compassed in the source text which is reflected into the
structure of the network. Obtained sets are evaluated on a manually annotated
keywords: for the set of extracted keyword candidates average F1 score is
24,63%, and average F2 score is 21,19%; for the exacted words-tuples candidates
average F1 score is 25,9% and average F2 score is 24,47%.
Flow for Meta Control
Authors: Vadim Bulitko
Date: 2014-07-17 00:00:00

The psychological state of flow has been linked to optimizing human
performance. A key condition of flow emergence is a match between the human
abilities and complexity of the task. We propose a simple computational model
of flow for Artificial Intelligence (AI) agents. The model factors the standard
agent-environment state into a self-reflective set of the agent's abilities and
a socially learned set of the environmental complexity. Maximizing the flow
serves as a meta control for the agent. We show how to apply the meta-control
policy to a broad class of AI control policies and illustrate our approach with
a specific implementation. Results in a synthetic testbed are promising and
open interesting directions for future work.
A feature construction framework based on outlier detection and discriminative pattern mining
Authors: Albrecht Zimmermann
Date: 2014-07-17 00:00:00
Tasks: Dimensionality Reduction, feature selection, Outlier Detection

No matter the expressive power and sophistication of supervised learning
algorithms, their effectiveness is restricted by the features describing the
data. This is not a new insight in ML and many methods for feature selection,
transformation, and construction have been developed. But while this is
on-going for general techniques for feature selection and transformation, i.e.
dimensionality reduction, work on feature construction, i.e. enriching the
data, is by now mainly the domain of image, particularly character,
recognition, and NLP.
  In this work, we propose a new general framework for feature construction.
The need for feature construction in a data set is indicated by class outliers
and discriminative pattern mining used to derive features on their
k-neighborhoods. We instantiate the framework with LOF and C4.5-Rules, and
evaluate the usefulness of the derived features on a diverse collection of UCI
data sets. The derived features are more often useful than ones derived by
DC-Fringe, and our approach is much less likely to overfit. But while a weak
learner, Naive Bayes, benefits strongly from the feature construction, the
effect is less pronounced for C4.5, and almost vanishes for an SVM leaner.
  Keywords: feature construction, classification, outlier detection
Optimization Under Uncertainty Using the Generalized Inverse Distribution Function
Authors: Domenico Quagliarella, Giovanni Petrone, Gianluca Iaccarino
Date: 2014-07-17 00:00:00

A framework for robust optimization under uncertainty based on the use of the
generalized inverse distribution function (GIDF), also called quantile
function, is here proposed. Compared to more classical approaches that rely on
the usage of statistical moments as deterministic attributes that define the
objectives of the optimization process, the inverse cumulative distribution
function allows for the use of all the possible information available in the
probabilistic domain. Furthermore, the use of a quantile based approach leads
naturally to a multi-objective methodology which allows an a-posteriori
selection of the candidate design based on risk/opportunity criteria defined by
the designer. Finally, the error on the estimation of the objectives due to the
resolution of the GIDF will be proven to be quantifiable
Virus Detection in Multiplexed Nanowire Arrays using Hidden Semi-Markov models
Authors: Shalini Ghosh, Patrick Lincoln, Christian Petersen, Alfonso Valdes
Date: 2014-07-16 00:00:00

In this paper, we address the problem of real-time detection of viruses
docking to nanowires, especially when multiple viruses dock to the same
nano-wire. The task becomes more complicated when there is an array of
nanowires coated with different antibodies, where different viruses can dock to
each coated nanowire at different binding strengths. We model the array
response to a viral agent as a pattern of conductance change over nanowires
with known modifier --- this representation permits analysis of the output of
such an array via belief network (Bayes) methods, as well as novel generative
models like the Hidden Semi-Markov Model (HSMM).
Sequential Logistic Principal Component Analysis (SLPCA): Dimensional Reduction in Streaming Multivariate Binary-State System
Authors: Zhaoyi Kang, Costas J. Spanos
Date: 2014-07-16 00:00:00

Sequential or online dimensional reduction is of interests due to the
explosion of streaming data based applications and the requirement of adaptive
statistical modeling, in many emerging fields, such as the modeling of energy
end-use profile. Principal Component Analysis (PCA), is the classical way of
dimensional reduction. However, traditional Singular Value Decomposition (SVD)
based PCA fails to model data which largely deviates from Gaussian
distribution. The Bregman Divergence was recently introduced to achieve a
generalized PCA framework. If the random variable under dimensional reduction
follows Bernoulli distribution, which occurs in many emerging fields, the
generalized PCA is called Logistic PCA (LPCA). In this paper, we extend the
batch LPCA to a sequential version (i.e. SLPCA), based on the sequential convex
optimization theory. The convergence property of this algorithm is discussed
compared to the batch version of LPCA (i.e. BLPCA), as well as its performance
in reducing the dimension for multivariate binary-state systems. Its
application in building energy end-use profile modeling is also investigated.
Subspace Restricted Boltzmann Machine
Authors: Jakub M. Tomczak, Adam Gonczarek
Date: 2014-07-16 00:00:00
Tasks: General Classification

The subspace Restricted Boltzmann Machine (subspaceRBM) is a third-order
Boltzmann machine where multiplicative interactions are between one visible and
two hidden units. There are two kinds of hidden units, namely, gate units and
subspace units. The subspace units reflect variations of a pattern in data and
the gate unit is responsible for activating the subspace units. Additionally,
the gate unit can be seen as a pooling feature. We evaluate the behavior of
subspaceRBM through experiments with MNIST digit recognition task, measuring
reconstruction error and classification error.
In Defense of MinHash Over SimHash
Authors: Anshumali Shrivastava, Ping Li
Date: 2014-07-16 00:00:00
Tasks: Retrieval, valid

MinHash and SimHash are the two widely adopted Locality Sensitive Hashing
(LSH) algorithms for large-scale data processing applications. Deciding which
LSH to use for a particular problem at hand is an important question, which has
no clear answer in the existing literature. In this study, we provide a
theoretical answer (validated by experiments) that MinHash virtually always
outperforms SimHash when the data are binary, as common in practice such as
search.
  The collision probability of MinHash is a function of resemblance similarity
($\mathcal{R}$), while the collision probability of SimHash is a function of
cosine similarity ($\mathcal{S}$). To provide a common basis for comparison, we
evaluate retrieval results in terms of $\mathcal{S}$ for both MinHash and
SimHash. This evaluation is valid as we can prove that MinHash is a valid LSH
with respect to $\mathcal{S}$, by using a general inequality $\mathcal{S}^2\leq
\mathcal{R}\leq \frac{\mathcal{S}}{2-\mathcal{S}}$. Our worst case analysis can
show that MinHash significantly outperforms SimHash in high similarity region.
  Interestingly, our intensive experiments reveal that MinHash is also
substantially better than SimHash even in datasets where most of the data
points are not too similar to each other. This is partly because, in practical
data, often $\mathcal{R}\geq \frac{\mathcal{S}}{z-\mathcal{S}}$ holds where $z$
is only slightly larger than 2 (e.g., $z\leq 2.1$). Our restricted worst case
analysis by assuming $\frac{\mathcal{S}}{z-\mathcal{S}}\leq \mathcal{R}\leq
\frac{\mathcal{S}}{2-\mathcal{S}}$ shows that MinHash indeed significantly
outperforms SimHash even in low similarity region.
  We believe the results in this paper will provide valuable guidelines for
search in practice, especially when the data are sparse.