pwc-archive/papers-with-abstracts huggingface dataset:
Improving the Interpretability of Support Vector Machines-based Fuzzy Rules
Authors: Duc-Hien Nguyen, Manh-Thanh Le
Date: 2014-08-22 00:00:00

Support vector machines (SVMs) and fuzzy rule systems are functionally
equivalent under some conditions. Therefore, the learning algorithms developed
in the field of support vector machines can be used to adapt the parameters of
fuzzy systems. Extracting fuzzy models from support vector machines has the
inherent advantage that the model does not need to determine the number of
rules in advance. However, after the support vector machine learning, the
complexity is usually high, and interpretability is also impaired. This paper
not only proposes a complete framework for extracting interpretable SVM-based
fuzzy modeling, but also provides optimization issues of the models.
Simulations examples are given to embody the idea of this paper.
A two-stage architecture for stock price forecasting by combining SOM and fuzzy-SVM
Authors: Duc-Hien Nguyen, Manh-Thanh Le
Date: 2014-08-22 00:00:00
Tasks: Clustering

This paper proposed a model to predict the stock price based on combining
Self-Organizing Map (SOM) and fuzzy-Support Vector Machines (f-SVM). Extraction
of fuzzy rules from raw data based on the combining of statistical machine
learning models is base of this proposed approach. In the proposed model, SOM
is used as a clustering algorithm to partition the whole input space into the
several disjoint regions. For each partition, a set of fuzzy rules is extracted
based on a f-SVM combining model. Then fuzzy rules sets are used to predict the
test data using fuzzy inference algorithms. The performance of the proposed
approach is compared with other models using four data sets
Gap-weighted subsequences for automatic cognate identification and phylogenetic inference
Authors: Taraka Rama
Date: 2014-08-11 00:00:00

In this paper, we describe the problem of cognate identification and its
relation to phylogenetic inference. We introduce subsequence based features for
discriminating cognates from non-cognates. We show that subsequence based
features perform better than the state-of-the-art string similarity measures
for the purpose of cognate identification. We use the cognate judgments for the
purpose of phylogenetic inference and observe that these classifiers infer a
tree which is close to the gold standard tree. The contribution of this paper
is the use of subsequence features for cognate identification and to employ the
cognate judgments for phylogenetic inference.
Uniform Sampling for Matrix Approximation
Authors: Michael B. Cohen, Yin Tat Lee, Cameron Musco, Christopher Musco, Richard Peng, Aaron Sidford
Date: 2014-08-21 00:00:00
Tasks: regression

Random sampling has become a critical tool in solving massive matrix
problems. For linear regression, a small, manageable set of data rows can be
randomly selected to approximate a tall, skinny data matrix, improving
processing time significantly. For theoretical performance guarantees, each row
must be sampled with probability proportional to its statistical leverage
score. Unfortunately, leverage scores are difficult to compute.
  A simple alternative is to sample rows uniformly at random. While this often
works, uniform sampling will eliminate critical row information for many
natural instances. We take a fresh look at uniform sampling by examining what
information it does preserve. Specifically, we show that uniform sampling
yields a matrix that, in some sense, well approximates a large fraction of the
original. While this weak form of approximation is not enough for solving
linear regression directly, it is enough to compute a better approximation.
  This observation leads to simple iterative row sampling algorithms for matrix
approximation that run in input-sparsity time and preserve row structure and
sparsity at all intermediate steps. In addition to an improved understanding of
uniform sampling, our main proof introduces a structural result of independent
interest: we show that every matrix can be made to have low coherence by
reweighting a small subset of its rows.
Deep Learning for Detecting Robotic Grasps
Authors: Ian Lenz, Honglak Lee, Ashutosh Saxena
Date: 2013-01-16 00:00:00
Tasks: Deep Learning, Robotic Grasping

We consider the problem of detecting robotic grasps in an RGB-D view of a
scene containing objects. In this work, we apply a deep learning approach to
solve this problem, which avoids time-consuming hand-design of features. This
presents two main challenges. First, we need to evaluate a huge number of
candidate grasps. In order to make detection fast, as well as robust, we
present a two-step cascaded structure with two deep networks, where the top
detections from the first are re-evaluated by the second. The first network has
fewer features, is faster to run, and can effectively prune out unlikely
candidate grasps. The second, with more features, is slower but has to run only
on the top few detections. Second, we need to handle multimodal inputs well,
for which we present a method to apply structured regularization on the weights
based on multimodal group regularization. We demonstrate that our method
outperforms the previous state-of-the-art methods in robotic grasp detection,
and can be used to successfully execute grasps on two different robotic
platforms.
A Case Study in Text Mining: Interpreting Twitter Data From World Cup Tweets
Authors: Daniel Godfrey, Caley Johns, Carl Meyer, Shaina Race, Carol Sadek
Date: 2014-08-21 00:00:00
Tasks: Clustering

Cluster analysis is a field of data analysis that extracts underlying
patterns in data. One application of cluster analysis is in text-mining, the
analysis of large collections of text to find similarities between documents.
We used a collection of about 30,000 tweets extracted from Twitter just before
the World Cup started. A common problem with real world text data is the
presence of linguistic noise. In our case it would be extraneous tweets that
are unrelated to dominant themes. To combat this problem, we created an
algorithm that combined the DBSCAN algorithm and a consensus matrix. This way
we are left with the tweets that are related to those dominant themes. We then
used cluster analysis to find those topics that the tweets describe. We
clustered the tweets using k-means, a commonly used clustering algorithm, and
Non-Negative Matrix Factorization (NMF) and compared the results. The two
algorithms gave similar results, but NMF proved to be faster and provided more
easily interpreted results. We explored our results using two visualization
tools, Gephi and Wordle.
On the Sample Complexity of Subspace Learning
Authors: Alessandro Rudi, Guille D. Canas, Lorenzo Rosasco
Date: 2014-08-21 00:00:00
Proceeding: NeurIPS 2013 12

A large number of algorithms in machine learning, from principal component
analysis (PCA), and its non-linear (kernel) extensions, to more recent spectral
embedding and support estimation methods, rely on estimating a linear subspace
from samples. In this paper we introduce a general formulation of this problem
and derive novel learning error estimates. Our results rely on natural
assumptions on the spectral properties of the covariance operator associated to
the data distribu- tion, and hold for a wide class of metrics between
subspaces. As special cases, we discuss sharp error estimates for the
reconstruction properties of PCA and spectral support estimation. Key to our
analysis is an operator theoretic approach that has broad applicability to
spectral learning methods.
A Study of Proxies for Shapley Allocations of Transport Costs
Authors: Haris Aziz, Casey Cahan, Charles Gretton, Phillip Kilby, Nicholas Mattei, Toby Walsh
Date: 2014-08-21 00:00:00

We propose and evaluate a number of solutions to the problem of calculating
the cost to serve each location in a single-vehicle transport setting. Such
cost to serve analysis has application both strategically and operationally in
transportation. The problem is formally given by the traveling salesperson game
(TSG), a cooperative total utility game in which agents correspond to locations
in a traveling salesperson problem (TSP). The cost to serve a location is an
allocated portion of the cost of an optimal tour. The Shapley value is one of
the most important normative division schemes in cooperative games, giving a
principled and fair allocation both for the TSG and more generally. We consider
a number of direct and sampling-based procedures for calculating the Shapley
value, and present the first proof that approximating the Shapley value of the
TSG within a constant factor is NP-hard. Treating the Shapley value as an ideal
baseline allocation, we then develop six proxies for that value which are
relatively easy to compute. We perform an experimental evaluation using
Synthetic Euclidean games as well as games derived from real-world tours
calculated for fast-moving consumer goods scenarios. Our experiments show that
several computationally tractable allocation techniques correspond to good
proxies for the Shapley value.
Image Retargeting by Content-Aware Synthesis
Authors: Weiming Dong, Fuzhang Wu, Yan Kong, Xing Mei, Tong-Yee Lee, Xiaopeng Zhang
Date: 2014-03-26 00:00:00
Tasks: Image Retargeting

Real-world images usually contain vivid contents and rich textural details,
which will complicate the manipulation on them. In this paper, we design a new
framework based on content-aware synthesis to enhance content-aware image
retargeting. By detecting the textural regions in an image, the textural image
content can be synthesized rather than simply distorted or cropped. This method
enables the manipulation of textural & non-textural regions with different
strategy since they have different natures. We propose to retarget the textural
regions by content-aware synthesis and non-textural regions by fast
multi-operators. To achieve practical retargeting applications for general
images, we develop an automatic and fast texture detection method that can
detect multiple disjoint textural regions. We adjust the saliency of the image
according to the features of the textural regions. To validate the proposed
method, comparisons with state-of-the-art image targeting techniques and a user
study were conducted. Convincing visual results are shown to demonstrate the
effectiveness of the proposed method.
Ranking via Robust Binary Classification and Parallel Parameter Estimation in Large-Scale Data
Authors: Hyokun Yun, Parameswaran Raman, S. V. N. Vishwanathan
Date: 2014-02-11 00:00:00
Tasks: Binary Classification, General Classification, Learning-To-Rank, parameter estimation, Robust classification

We propose RoBiRank, a ranking algorithm that is motivated by observing a
close connection between evaluation metrics for learning to rank and loss
functions for robust classification. The algorithm shows a very competitive
performance on standard benchmark datasets against other representative
algorithms in the literature. On the other hand, in large scale problems where
explicit feature vectors and scores are not given, our algorithm can be
efficiently parallelized across a large number of machines; for a task that
requires 386,133 x 49,824,519 pairwise interactions between items to be ranked,
our algorithm finds solutions that are of dramatically higher quality than that
can be found by a state-of-the-art competitor algorithm, given the same amount
of wall-clock time for computation.
Swarm Intelligence Based Multi-phase OPF For Peak Power Loss Reduction In A Smart Grid
Authors: Adnan Anwar, A. N. Mahmood
Date: 2014-08-21 00:00:00

Recently there has been increasing interest in improving smart grids
efficiency using computational intelligence. A key challenge in future smart
grid is designing Optimal Power Flow tool to solve important planning problems
including optimal DG capacities. Although, a number of OPF tools exists for
balanced networks there is a lack of research for unbalanced multi-phase
distribution networks. In this paper, a new OPF technique has been proposed for
the DG capacity planning of a smart grid. During the formulation of the
proposed algorithm, multi-phase power distribution system is considered which
has unbalanced loadings, voltage control and reactive power compensation
devices. The proposed algorithm is built upon a co-simulation framework that
optimizes the objective by adapting a constriction factor Particle Swarm
optimization. The proposed multi-phase OPF technique is validated using IEEE
8500-node benchmark distribution system.
Enhanced Estimation of Autoregressive Wind Power Prediction Model Using Constriction Factor Particle Swarm Optimization
Authors: Adnan Anwar, Abdun Naser Mahmood
Date: 2014-08-21 00:00:00

Accurate forecasting is important for cost-effective and efficient monitoring
and control of the renewable energy based power generation. Wind based power is
one of the most difficult energy to predict accurately, due to the widely
varying and unpredictable nature of wind energy. Although Autoregressive (AR)
techniques have been widely used to create wind power models, they have shown
limited accuracy in forecasting, as well as difficulty in determining the
correct parameters for an optimized AR model. In this paper, Constriction
Factor Particle Swarm Optimization (CF-PSO) is employed to optimally determine
the parameters of an Autoregressive (AR) model for accurate prediction of the
wind power output behaviour. Appropriate lag order of the proposed model is
selected based on Akaike information criterion. The performance of the proposed
PSO based AR model is compared with four well-established approaches;
Forward-backward approach, Geometric lattice approach, Least-squares approach
and Yule-Walker approach, that are widely used for error minimization of the AR
model. To validate the proposed approach, real-life wind power data of
\textit{Capital Wind Farm} was obtained from Australian Energy Market Operator.
Experimental evaluation based on a number of different datasets demonstrate
that the performance of the AR model is significantly improved compared with
benchmark methods.
A Bi-clustering Framework for Consensus Problems
Authors: Mariano Tepper, Guillermo Sapiro
Date: 2014-04-30 00:00:00
Tasks: Clustering, Community Detection

We consider grouping as a general characterization for problems such as
clustering, community detection in networks, and multiple parametric model
estimation. We are interested in merging solutions from different grouping
algorithms, distilling all their good qualities into a consensus solution. In
this paper, we propose a bi-clustering framework and perspective for reaching
consensus in such grouping problems. In particular, this is the first time that
the task of finding/fitting multiple parametric models to a dataset is formally
posed as a consensus problem. We highlight the equivalence of these tasks and
establish the connection with the computational Gestalt program, that seeks to
provide a psychologically-inspired detection theory for visual events. We also
present a simple but powerful bi-clustering algorithm, specially tuned to the
nature of the problem we address, though general enough to handle many
different instances inscribed within our characterization. The presentation is
accompanied with diverse and extensive experimental results in clustering,
community detection, and multiple parametric model estimation in image
processing applications.
On minimal sets of graded attribute implications
Authors: Vilem Vychodil
Date: 2014-05-27 00:00:00
Tasks: Attribute

We explore the structure of non-redundant and minimal sets consisting of
graded if-then rules. The rules serve as graded attribute implications in
object-attribute incidence data and as similarity-based functional dependencies
in a similarity-based generalization of the relational model of data. Based on
our observations, we derive a polynomial-time algorithm which transforms a
given finite set of rules into an equivalent one which has the least size in
terms of the number of rules.
Be Careful When Assuming the Obvious: Commentary on "The placement of the head that minimizes online memory: a complex systems approach"
Authors: Phillip M. Alday
Date: 2014-08-20 00:00:00

Ferrer-i-Cancho (2015) presents a mathematical model of both the synchronic
and diachronic nature of word order based on the assumption that memory costs
are a never decreasing function of distance and a few very general linguistic
assumptions. However, even these minimal and seemingly obvious assumptions are
not as safe as they appear in light of recent typological and psycholinguistic
evidence. The interaction of word order and memory has further depths to be
explored.
DeepPose: Human Pose Estimation via Deep Neural Networks
Authors: Alexander Toshev, Christian Szegedy
Date: 2013-12-17 00:00:00
Proceeding: CVPR 2014 6
Tasks: Pose Estimation, regression

We propose a method for human pose estimation based on Deep Neural Networks
(DNNs). The pose estimation is formulated as a DNN-based regression problem
towards body joints. We present a cascade of such DNN regressors which results
in high precision pose estimates. The approach has the advantage of reasoning
about pose in a holistic fashion and has a simple but yet powerful formulation
which capitalizes on recent advances in Deep Learning. We present a detailed
empirical analysis with state-of-art or better performance on four academic
benchmarks of diverse real-world images.
Code Generation for High-Level Synthesis of Multiresolution Applications on FPGAs
Authors: Moritz Schmid, Oliver Reiche, Christian Schmitt, Frank Hannig, Jürgen Teich
Date: 2014-08-20 00:00:00
Tasks: Code Generation, High-Level Synthesis, Vocal Bursts Intensity Prediction

Multiresolution Analysis (MRA) is a mathematical method that is based on
working on a problem at different scales. One of its applications is medical
imaging where processing at multiple scales, based on the concept of Gaussian
and Laplacian image pyramids, is a well-known technique. It is often applied to
reduce noise while preserving image detail on different levels of granularity
without modifying the filter kernel. In scientific computing, multigrid methods
are a popular choice, as they are asymptotically optimal solvers for elliptic
Partial Differential Equations (PDEs). As such algorithms have a very high
computational complexity that would overwhelm CPUs in the presence of real-time
constraints, application-specific processors come into consideration for
implementation. Despite of huge advancements in leveraging productivity in the
respective fields, designers are still required to have detailed knowledge
about coding techniques and the targeted architecture to achieve efficient
solutions. Recently, the HIPAcc framework was proposed as a means for automatic
code generation of image processing algorithms, based on a Domain-Specific
Language (DSL). From the same code base, it is possible to generate code for
efficient implementations on several accelerator technologies including
different types of Graphics Processing Units (GPUs) as well as reconfigurable
logic (FPGAs). In this work, we demonstrate the ability of HIPAcc to generate
code for the implementation of multiresolution applications on FPGAs and
embedded GPUs.
Conic Multi-Task Classification
Authors: Cong Li, Michael Georgiopoulos, Georgios C. Anagnostopoulos
Date: 2014-08-20 00:00:00
Tasks: Classification, General Classification, Multi-Task Learning

Traditionally, Multi-task Learning (MTL) models optimize the average of
task-related objective functions, which is an intuitive approach and which we
will be referring to as Average MTL. However, a more general framework,
referred to as Conic MTL, can be formulated by considering conic combinations
of the objective functions instead; in this framework, Average MTL arises as a
special case, when all combination coefficients equal 1. Although the advantage
of Conic MTL over Average MTL has been shown experimentally in previous works,
no theoretical justification has been provided to date. In this paper, we
derive a generalization bound for the Conic MTL method, and demonstrate that
the tightest bound is not necessarily achieved, when all combination
coefficients equal 1; hence, Average MTL may not always be the optimal choice,
and it is important to consider Conic MTL. As a byproduct of the generalization
bound, it also theoretically explains the good experimental results of previous
relevant works. Finally, we propose a new Conic MTL model, whose conic
combination coefficients minimize the generalization bound, instead of choosing
them heuristically as has been done in previous methods. The rationale and
advantage of our model is demonstrated and verified via a series of experiments
by comparing with several other methods.
GIMP and Wavelets for Medical Image Processing: Enhancing Images of the Fundus of the Eye
Authors: Amelia Carolina Sparavigna
Date: 2014-08-20 00:00:00
Tasks: Image Manipulation, Image Segmentation, Segmentation, Semantic Segmentation

The visual analysis of retina and of its vascular characteristics is
important in the diagnosis and monitoring of diseases of visual perception. In
the related medical diagnoses, the digital processing of the fundus images is
used to obtain the segmentation of retinal vessels. However, an image
segmentation is often requiring methods based on peculiar or complex
algorithms: in this paper we will show some alternative approaches obtained by
applying freely available tools to enhance, without a specific segmentation,
the images of the fundus of the eye. We will see in particular, that combining
the use of GIMP, the GNU Image Manipulation Program, with the wavelet filter of
Iris, a program well-known for processing astronomical images, the result is
giving images which can be alternative of those obtained from segmentation.
Seeing through bag-of-visual-word glasses: towards understanding quantization effects in feature extraction methods
Authors: Alexander Freytag, Johannes Rühle, Paul Bodesheim, Erik Rodner, Joachim Denzler
Date: 2014-08-20 00:00:00
Tasks: image-classification, Image Classification, Quantization

Vector-quantized local features frequently used in bag-of-visual-words
approaches are the backbone of popular visual recognition systems due to both
their simplicity and their performance. Despite their success,
bag-of-words-histograms basically contain low-level image statistics (e.g.,
number of edges of different orientations). The question remains how much
visual information is "lost in quantization" when mapping visual features to
code words? To answer this question, we present an in-depth analysis of the
effect of local feature quantization on human recognition performance. Our
analysis is based on recovering the visual information by inverting quantized
local features and presenting these visualizations with different codebook
sizes to human observers. Although feature inversion techniques are around for
quite a while, to the best of our knowledge, our technique is the first
visualizing especially the effect of feature quantization. Thereby, we are now
able to compare single steps in common image classification pipelines to human
counterparts.
Incremental Cardinality Constraints for MaxSAT
Authors: Ruben Martins, Saurabh Joshi, Vasco Manquinho, Ines Lynce
Date: 2014-08-20 00:00:00

Maximum Satisfiability (MaxSAT) is an optimization variant of the Boolean
Satisfiability (SAT) problem. In general, MaxSAT algorithms perform a
succession of SAT solver calls to reach an optimum solution making extensive
use of cardinality constraints. Many of these algorithms are non-incremental in
nature, i.e. at each iteration the formula is rebuilt and no knowledge is
reused from one iteration to another. In this paper, we exploit the knowledge
acquired across iterations using novel schemes to use cardinality constraints
in an incremental fashion. We integrate these schemes with several MaxSAT
algorithms. Our experimental results show a significant performance boost for
these algo- rithms as compared to their non-incremental counterparts. These
results suggest that incremental cardinality constraints could be beneficial
for other constraint solving domains.