pwc-archive/papers-with-abstracts huggingface dataset:
Confidence Sets Based on Thresholding Estimators in High-Dimensional Gaussian Regression Models
Authors: Ulrike Schneider
Date: 2013-08-14 00:00:00
Tasks: regression, Variable Selection

We study confidence intervals based on hard-thresholding, soft-thresholding,
and adaptive soft-thresholding in a linear regression model where the number of
regressors $k$ may depend on and diverge with sample size $n$. In addition to
the case of known error variance, we define and study versions of the
estimators when the error variance is unknown. In the known variance case, we
provide an exact analysis of the coverage properties of such intervals in
finite samples. We show that these intervals are always larger than the
standard interval based on the least-squares estimator. Asymptotically, the
intervals based on the thresholding estimators are larger even by an order of
magnitude when the estimators are tuned to perform consistent variable
selection. For the unknown-variance case, we provide non-trivial lower bounds
for the coverage probabilities in finite samples and conduct an asymptotic
analysis where the results from the known-variance case can be shown to carry
over asymptotically if the number of degrees of freedom $n-k$ tends to infinity
fast enough in relation to the thresholding parameter.
Speech earthquakes: scaling and universality in human voice
Authors: Jordi Luque, Bartolo Luque, Lucas Lacasa
Date: 2014-08-05 00:00:00
Tasks: Speech Synthesis

Speech is a distinctive complex feature of human capabilities. In order to
understand the physics underlying speech production, in this work we
empirically analyse the statistics of large human speech datasets ranging
several languages. We first show that during speech the energy is unevenly
released and power-law distributed, reporting a universal robust
Gutenberg-Richter-like law in speech. We further show that such earthquakes in
speech show temporal correlations, as the interevent statistics are again
power-law distributed. Since this feature takes place in the intra-phoneme
range, we conjecture that the responsible for this complex phenomenon is not
cognitive, but it resides on the physiological speech production mechanism.
Moreover, we show that these waiting time distributions are scale invariant
under a renormalisation group transformation, suggesting that the process of
speech generation is indeed operating close to a critical point. These results
are put in contrast with current paradigms in speech processing, which point
towards low dimensional deterministic chaos as the origin of nonlinear traits
in speech fluctuations. As these latter fluctuations are indeed the aspects
that humanize synthetic speech, these findings may have an impact in future
speech synthesis technologies. Results are robust and independent of the
communication language or the number of speakers, pointing towards an universal
pattern and yet another hint of complexity in human speech.
A Flexible Iterative Framework for Consensus Clustering
Authors: Shaina Race, Carl Meyer
Date: 2014-08-05 00:00:00
Tasks: Clustering, Dimensionality Reduction

A novel framework for consensus clustering is presented which has the ability
to determine both the number of clusters and a final solution using multiple
algorithms. A consensus similarity matrix is formed from an ensemble using
multiple algorithms and several values for k. A variety of dimension reduction
techniques and clustering algorithms are considered for analysis. For noisy or
high-dimensional data, an iterative technique is presented to refine this
consensus matrix in way that encourages algorithms to agree upon a common
solution. We utilize the theory of nearly uncoupled Markov chains to determine
the number, k , of clusters in a dataset by considering a random walk on the
graph defined by the consensus matrix. The eigenvalues of the associated
transition probability matrix are used to determine the number of clusters.
This method succeeds at determining the number of clusters in many datasets
where previous methods fail. On every considered dataset, our consensus method
provides a final result with accuracy well above the average of the individual
algorithms.
Determining the Number of Clusters via Iterative Consensus Clustering
Authors: Shaina Race, Carl Meyer, Kevin Valakuzhy
Date: 2014-08-05 00:00:00
Tasks: Clustering

We use a cluster ensemble to determine the number of clusters, k, in a group
of data. A consensus similarity matrix is formed from the ensemble using
multiple algorithms and several values for k. A random walk is induced on the
graph defined by the consensus matrix and the eigenvalues of the associated
transition probability matrix are used to determine the number of clusters. For
noisy or high-dimensional data, an iterative technique is presented to refine
this consensus matrix in way that encourages a block-diagonal form. It is shown
that the resulting consensus matrix is generally superior to existing
similarity matrices for this type of spectral analysis.
DuSK: A Dual Structure-preserving Kernel for Supervised Tensor Learning with Applications to Neuroimages
Authors: Lifang He, Xiangnan Kong, Philip S. Yu, Ann B. Ragin, Zhifeng Hao, Xiaowei Yang
Date: 2014-07-31 00:00:00
Tasks: General Classification

With advances in data collection technologies, tensor data is assuming
increasing prominence in many applications and the problem of supervised tensor
learning has emerged as a topic of critical significance in the data mining and
machine learning community. Conventional methods for supervised tensor learning
mainly focus on learning kernels by flattening the tensor into vectors or
matrices, however structural information within the tensors will be lost. In
this paper, we introduce a new scheme to design structure-preserving kernels
for supervised tensor learning. Specifically, we demonstrate how to leverage
the naturally available structure within the tensorial representation to encode
prior knowledge in the kernel. We proposed a tensor kernel that can preserve
tensor structures based upon dual-tensorial mapping. The dual-tensorial mapping
function can map each tensor instance in the input space to another tensor in
the feature space while preserving the tensorial structure. Theoretically, our
approach is an extension of the conventional kernels in the vector space to
tensor space. We applied our novel kernel in conjunction with SVM to real-world
tensor classification problems including brain fMRI classification for three
different diseases (i.e., Alzheimer's disease, ADHD and brain damage by HIV).
Extensive empirical studies demonstrate that our proposed approach can
effectively boost tensor classification performances, particularly with small
sample sizes.
Real-Time Traffic Signal Control for Modern Roundabouts by Using Particle Swarm Optimization-Based Fuzzy Controller
Authors: Yue-Jiao Gong, Jun Zhang
Date: 2014-08-04 00:00:00
Tasks: Traffic Signal Control

Due to that the existing traffic facilities can hardly be extended,
developing traffic signal control methods is the most important way to improve
the traffic efficiency of modern roundabouts. This paper proposes a novel
traffic signal controller with two fuzzy layers for signalizing the roundabout.
The outer layer of the controller computes urgency degrees of all the phase
subsets and then activates the most urgent subset. This mechanism helps to
instantly respond to the current traffic condition of the roundabout so as to
improve real-timeness. The inner layer of the controller computes extension
time of the current phase. If the extension value is larger than a threshold
value, the current phase is maintained; otherwise the next phase in the running
phase subset (selected by the outer layer) is activated. The inner layer adopts
well-designed phase sequences, which helps to smooth the traffic flows and to
avoid traffic jam. In general, the proposed traffic signal controller is
capable of improving real-timeness as well as reducing traffic congestion.
Moreover, an offline particle swarm optimization (PSO) algorithm is developed
to optimize the membership functions adopted in the proposed controller. By
using optimal membership functions, the performance of the controller can be
further improved. Simulation results demonstrate that the proposed controller
outperforms previous traffic signal controllers in terms of improving the
traffic efficiency of modern roundabouts.
Estimating Maximally Probable Constrained Relations by Mathematical Programming
Authors: Lizhen Qu, Bjoern Andres
Date: 2014-08-04 00:00:00
Tasks: Clustering, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Relation

Estimating a constrained relation is a fundamental problem in machine
learning. Special cases are classification (the problem of estimating a map
from a set of to-be-classified elements to a set of labels), clustering (the
problem of estimating an equivalence relation on a set) and ranking (the
problem of estimating a linear order on a set). We contribute a family of
probability measures on the set of all relations between two finite, non-empty
sets, which offers a joint abstraction of multi-label classification,
correlation clustering and ranking by linear ordering. Estimating (learning) a
maximally probable measure, given (a training set of) related and unrelated
pairs, is a convex optimization problem. Estimating (inferring) a maximally
probable relation, given a measure, is a 01-linear program. It is solved in
linear time for maps. It is NP-hard for equivalence relations and linear
orders. Practical solutions for all three cases are shown in experiments with
real data. Finally, estimating a maximally probable measure and relation
jointly is posed as a mixed-integer nonlinear program. This formulation
suggests a mathematical programming approach to semi-supervised learning.
Targetable Named Entity Recognition in Social Media
Authors: Sandeep Ashwini, Jinho D. Choi
Date: 2014-08-04 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

We present a novel approach for recognizing what we call targetable named
entities; that is, named entities in a targeted set (e.g, movies, books, TV
shows). Unlike many other NER systems that need to retrain their statistical
models as new entities arrive, our approach does not require such retraining,
which makes it more adaptable for types of entities that are frequently
updated. For this preliminary study, we focus on one entity type, movie title,
using data collected from Twitter. Our system is tested on two evaluation sets,
one including only entities corresponding to movies in our training set, and
the other excluding any of those entities. Our final model shows F1-scores of
76.19% and 78.70% on these evaluation sets, which gives strong evidence that
our approach is completely unbiased to any par- ticular set of entities found
during training.
Multithreshold Entropy Linear Classifier
Authors: Wojciech Marian Czarnecki, Jacek Tabor
Date: 2014-08-04 00:00:00
Tasks: Activity Prediction

Linear classifiers separate the data with a hyperplane. In this paper we
focus on the novel method of construction of multithreshold linear classifier,
which separates the data with multiple parallel hyperplanes. Proposed model is
based on the information theory concepts -- namely Renyi's quadratic entropy
and Cauchy-Schwarz divergence.
  We begin with some general properties, including data scale invariance. Then
we prove that our method is a multithreshold large margin classifier, which
shows the analogy to the SVM, while in the same time works with much broader
class of hypotheses. What is also interesting, proposed method is aimed at the
maximization of the balanced quality measure (such as Matthew's Correlation
Coefficient) as opposed to very common maximization of the accuracy. This
feature comes directly from the optimization problem statement and is further
confirmed by the experiments on the UCI datasets.
  It appears, that our Multithreshold Entropy Linear Classifier (MELC) obtaines
similar or higher scores than the ones given by SVM on both synthetic and real
data. We show how proposed approach can be benefitial for the cheminformatics
in the task of ligands activity prediction, where despite better classification
results, MELC gives some additional insight into the data structure (classes of
underrepresented chemical compunds).
Computational Analysis of Perfect-Information Position Auctions
Authors: David R. M Thompson, Kevin Leyton-Brown
Date: 2014-08-04 00:00:00
Tasks: Position

After experimentation with other designs, the major search engines converged
on the weighted, generalized second-price auction (wGSP) for selling keyword
advertisements. Notably, this convergence occurred before position auctions
were well understood (or, indeed, widely studied) theoretically. While much
progress has been made since, theoretical analysis is still not able to settle
the question of why search engines found wGSP preferable to other position
auctions. We approach this question in a new way, adopting a new analytical
paradigm we dub "computational mechanism analysis." By sampling position
auction games from a given distribution, encoding them in a computationally
efficient representation language, computing their Nash equilibria, and then
calculating economic quantities of interest, we can quantitatively answer
questions that theoretical methods have not. We considered seven widely studied
valuation models from the literature and three position auction variants
(generalized first price, unweighted generalized second price, and wGSP). We
found that wGSP consistently showed the best ads of any position auction,
measured both by social welfare and by relevance (expected number of clicks).
Even in models where wGSP was already known to have bad worse-case efficiency,
we found that it almost always performed well on average. In contrast, we found
that revenue was extremely variable across auction mechanisms, and was highly
sensitive to equilibrium selection, the preference model, and the valuation
distribution.
A Pattern Recognition System for Detecting Use of Mobile Phones While Driving
Authors: Rafael A. Berri, Alexandre G. Silva, Rafael S. Parpinelli, Elaine Girardi, Rangel Arthur
Date: 2014-08-04 00:00:00
Tasks: General Classification

It is estimated that 80% of crashes and 65% of near collisions involved
drivers inattentive to traffic for three seconds before the event. This paper
develops an algorithm for extracting characteristics allowing the cell phones
identification used during driving a vehicle. Experiments were performed on
sets of images with 100 positive images (with phone) and the other 100 negative
images (no phone), containing frontal images of the driver. Support Vector
Machine (SVM) with Polynomial kernel is the most advantageous classification
system to the features provided by the algorithm, obtaining a success rate of
91.57% for the vision system. Tests done on videos show that it is possible to
use the image datasets for training classifiers in real situations. Periods of
3 seconds were correctly classified at 87.43% of cases.
Boundary properties of the inconsistency of pairwise comparisons in group decisions
Authors: Matteo Brunelli, Michele Fedrizzi
Date: 2014-08-04 00:00:00

This paper proposes an analysis of the effects of consensus and preference
aggregation on the consistency of pairwise comparisons. We define some boundary
properties for the inconsistency of group preferences and investigate their
relation with different inconsistency indices. Some results are presented on
more general dependencies between properties of inconsistency indices and the
satisfaction of boundary properties. In the end, given three boundary
properties and nine indices among the most relevant ones, we will be able to
present a complete analysis of what indices satisfy what properties and offer a
reflection on the interpretation of the inconsistency of group preferences.
A Multi-Stage Supply Chain Network Optimization Using Genetic Algorithms
Authors: Nelson Christopher Dzupire, Yaw Nkansah-Gyekye
Date: 2014-08-04 00:00:00
Tasks: Evolutionary Algorithms

In today's global business market place, individual firms no longer compete
as independent entities with unique brand names but as integral part of supply
chain links. Key to success of any business is satisfying customer's demands on
time which may result in cost reductions and increase in service level. In
supply chain networks decisions are made with uncertainty about product's
demands, costs, prices, lead times, quality in a competitive and collaborative
environment. If poor decisions are made, they may lead to excess inventories
that are costly or to insufficient inventory that cannot meet customer's
demands. In this work we developed a bi-objective model that minimizes system
wide costs of the supply chain and delays on delivery of products to
distribution centers for a three echelon supply chain. Picking a set of Pareto
front for multi-objective optimization problems require robust and efficient
methods that can search an entire space. We used evolutionary algorithms to
find the set of Pareto fronts which have proved to be effective in finding the
entire set of Pareto fronts.
Engineering Crowdsourced Stream Processing Systems
Authors: Muhammad Imran, Ioanna Lykourentzou, Yannick Naudet, Carlos Castillo
Date: 2013-10-21 00:00:00

A crowdsourced stream processing system (CSP) is a system that incorporates
crowdsourced tasks in the processing of a data stream. This can be seen as
enabling crowdsourcing work to be applied on a sample of large-scale data at
high speed, or equivalently, enabling stream processing to employ human
intelligence. It also leads to a substantial expansion of the capabilities of
data processing systems. Engineering a CSP system requires the combination of
human and machine computation elements. From a general systems theory
perspective, this means taking into account inherited as well as emerging
properties from both these elements. In this paper, we position CSP systems
within a broader taxonomy, outline a series of design principles and evaluation
metrics, present an extensible framework for their design, and describe several
design patterns. We showcase the capabilities of CSP systems by performing a
case study that applies our proposed framework to the design and analysis of a
real system (AIDR) that classifies social media messages during time-critical
crisis events. Results show that compared to a pure stream processing system,
AIDR can achieve a higher data classification accuracy, while compared to a
pure crowdsourcing solution, the system makes better use of human workers by
requiring much less manual work effort.
Correlating and Cross-linking Knowledge Threads in Informledge System for Creating New Knowledge
Authors: T. R. Gopalakrishnan Nair, Meenakshi Malhotra
Date: 2014-08-04 00:00:00
Tasks: Retrieval

There has been a considerable advance in computing, to mimic the way in which
the brain tries to comprehend and structure the information to retrieve
meaningful knowledge. It is identified that neuronal entities hold whole of the
knowledge that the species makes use of. We intended to develop a modified
knowledge based system, termed as Informledge System (ILS) with autonomous
nodes and intelligent links that integrate and structure the pieces of
knowledge. We conceive that every piece of knowledge is a cluster of
cross-linked and correlated structure. In this paper, we put forward the theory
of the nodes depicting concepts, referred as Entity Concept State which in turn
is dealt with Concept State Diagrams (CSD). This theory is based on an abstract
framework provided by the concepts. The framework represents the ILS as the
weighted graph where the weights attached with the linked nodes help in
knowledge retrieval by providing the direction of connectivity of autonomous
nodes present in knowledge thread traversal. Here for the first time in the
process of developing Informledge, we apply tenor computation for creating
intelligent combinatorial knowledge with cross mutation to create fresh
knowledge which looks to be the fundamentals of a typical thought process.
Adapted Approach for Fruit Disease Identification using Images
Authors: Shiv Ram Dubey, Anand Singh Jalal
Date: 2014-05-20 00:00:00
Tasks: Clustering

Diseases in fruit cause devastating problem in economic losses and production
in agricultural industry worldwide. In this paper, an adaptive approach for the
identification of fruit diseases is proposed and experimentally validated. The
image processing based proposed approach is composed of the following main
steps; in the first step K-Means clustering technique is used for the defect
segmentation, in the second step some state of the art features are extracted
from the segmented image, and finally images are classified into one of the
classes by using a Multi-class Support Vector Machine. We have considered
diseases of apple as a test case and evaluated our approach for three types of
apple diseases namely apple scab, apple blotch and apple rot. Our experimental
results express that the proposed solution can significantly support accurate
detection and automatic identification of fruit diseases. The classification
accuracy for the proposed solution is achieved up to 93%.
Implementation of a language driven Backpropagation algorithm
Authors: I. V. Grossu, C. I. Ciuluvica
Date: 2013-09-23 00:00:00

Inspired by the importance of both communication and feedback on errors in
human learning, our main goal was to implement a similar mechanism in
supervised learning of artificial neural networks. The starting point in our
study was the observation that words should accompany the input vectors
included in the training set, thus extending the ANN input space. This had as
consequence the necessity to take into consideration a modified sigmoid
activation function for neurons in the first hidden layer (in agreement with a
specific MLP apartment structure), and also a modified version of the
Backpropagation algorithm, which allows using of unspecified (null) desired
output components. Following the belief that basic concepts should be tested on
simple examples, the previous mentioned mechanism was applied on both the XOR
problem and a didactic color case study. In this context, we noticed the
interesting fact that the ANN was capable to categorize all desired input
vectors in the absence of their corresponding words, even though the training
set included only word accompanied inputs, in both positive and negative
examples. Further analysis along applying this approach to more complex
scenarios is currently in progress, as we consider the proposed language-driven
algorithm might contribute to a better understanding of learning in humans,
opening as well the possibility to create a specific category of artificial
neural networks, with abstraction capabilities.