pwc-archive/papers-with-abstracts huggingface dataset:
Places205-VGGNet Models for Scene Recognition
Authors: Limin Wang, Sheng Guo, Weilin Huang, Yu Qiao
Date: 2015-08-07 00:00:00
Tasks: Computational Efficiency, GPU, Object Recognition, Scene Recognition

VGGNets have turned out to be effective for object recognition in still
images. However, it is unable to yield good performance by directly adapting
the VGGNet models trained on the ImageNet dataset for scene recognition. This
report describes our implementation of training the VGGNets on the large-scale
Places205 dataset. Specifically, we train three VGGNet models, namely
VGGNet-11, VGGNet-13, and VGGNet-16, by using a Multi-GPU extension of Caffe
toolbox with high computational efficiency. We verify the performance of
trained Places205-VGGNet models on three datasets: MIT67, SUN397, and
Places205. Our trained models achieve the state-of-the-art performance on these
datasets and are made public available.
The Contribution of Internal and Model Variabilities to the Uncertainty in CMIP5 Decadal Climate Predictions
Authors: Ehud Strobach, Golan Bel
Date: 2015-08-07 00:00:00

Decadal climate predictions, which are initialized with observed conditions,
are characterized by two main sources of uncertainties--internal and model
variabilities. Using an ensemble of climate model simulations from the CMIP5
decadal experiments, we quantified the total uncertainty associated with these
predictions and the relative importance of each source. Annual and monthly
averages of the surface temperature and wind components were considered. We
show that different definitions of the anomaly results in different conclusions
regarding the variance of the ensemble members. However, some features of the
uncertainty are common to all the measures we considered. We found that over
decadal time scales, there is no considerable increase in the uncertainty with
time. The model variability is more sensitive to the annual cycle than the
internal variability. This, in turn, results in a maximal uncertainty during
the winter in the northern hemisphere. The uncertainty of the surface
temperature prediction is dominated by the model variability, whereas the
uncertainty of the wind components is determined by both sources. Analysis of
the spatial distribution of the uncertainty reveals that the surface
temperature has higher variability over land and in high latitudes, whereas the
surface zonal wind has higher variability over the ocean. The relative
importance of the internal and model variabilities depends on the averaging
period, the definition of the anomaly, and the location. These findings suggest
that several methods should be combined in order to assess future climate
prediction uncertainties and that weighting schemes of the ensemble members may
reduce the uncertainties.
Sublinear Partition Estimation
Authors: Pushpendre Rastogi, Benjamin Van Durme
Date: 2015-08-07 00:00:00
Tasks: Language Modeling, Language Modelling, Object Recognition

The output scores of a neural network classifier are converted to
probabilities via normalizing over the scores of all competing categories.
Computing this partition function, $Z$, is then linear in the number of
categories, which is problematic as real-world problem sets continue to grow in
categorical types, such as in visual object recognition or discriminative
language modeling. We propose three approaches for sublinear estimation of the
partition function, based on approximate nearest neighbor search and kernel
feature maps and compare the performance of the proposed approaches
empirically.
A Mood-based Genre Classification of Television Content
Authors: Humberto Corona, Michael P. O'Mahony
Date: 2015-08-06 00:00:00
Tasks: Classification, General Classification, Genre classification, Navigate

The classification of television content helps users organise and navigate
through the large list of channels and programs now available. In this paper,
we address the problem of television content classification by exploiting text
information extracted from program transcriptions. We present an analysis which
adapts a model for sentiment that has been widely and successfully applied in
other fields such as music or blog posts. We use a real-world dataset obtained
from the Boxfish API to compare the performance of classifiers trained on a
number of different feature sets. Our experiments show that, over a large
collection of television content, program genres can be represented in a
three-dimensional space of valence, arousal and dominance, and that promising
classification results can be achieved using features based on this
representation. This finding supports the use of the proposed representation of
television content as a feature space for similarity computation and
recommendation generation.
A Knowledge Gradient Policy for Sequencing Experiments to Identify the Structure of RNA Molecules Using a Sparse Additive Belief Model
Authors: Yan Li, Kristofer G. Reyes, Jorge Vazquez-Anderson, Yingfei Wang, Lydia M. Contreras, Warren B. Powell
Date: 2015-08-06 00:00:00

We present a sparse knowledge gradient (SpKG) algorithm for adaptively
selecting the targeted regions within a large RNA molecule to identify which
regions are most amenable to interactions with other molecules. Experimentally,
such regions can be inferred from fluorescence measurements obtained by binding
a complementary probe with fluorescence markers to the targeted regions. We use
a biophysical model which shows that the fluorescence ratio under the log scale
has a sparse linear relationship with the coefficients describing the
accessibility of each nucleotide, since not all sites are accessible (due to
the folding of the molecule). The SpKG algorithm uniquely combines the Bayesian
ranking and selection problem with the frequentist $\ell_1$ regularized
regression approach Lasso. We use this algorithm to identify the sparsity
pattern of the linear model as well as sequentially decide the best regions to
test before experimental budget is exhausted. Besides, we also develop two
other new algorithms: batch SpKG algorithm, which generates more suggestions
sequentially to run parallel experiments; and batch SpKG with a procedure which
we call length mutagenesis. It dynamically adds in new alternatives, in the
form of types of probes, are created by inserting, deleting or mutating
nucleotides within existing probes. In simulation, we demonstrate these
algorithms on the Group I intron (a mid-size RNA molecule), showing that they
efficiently learn the correct sparsity pattern, identify the most accessible
region, and outperform several other policies.
Theoretical and Empirical Analysis of a Parallel Boosting Algorithm
Authors: Uday Kamath, Carlotta Domeniconi, Kenneth De Jong
Date: 2015-08-06 00:00:00
Tasks: Meta-Learning

Many real-world problems involve massive amounts of data. Under these
circumstances learning algorithms often become prohibitively expensive, making
scalability a pressing issue to be addressed. A common approach is to perform
sampling to reduce the size of the dataset and enable efficient learning.
Alternatively, one customizes learning algorithms to achieve scalability. In
either case, the key challenge is to obtain algorithmic efficiency without
compromising the quality of the results. In this paper we discuss a
meta-learning algorithm (PSBML) which combines features of parallel algorithms
with concepts from ensemble and boosting methodologies to achieve the desired
scalability property. We present both theoretical and empirical analyses which
show that PSBML preserves a critical property of boosting, specifically,
convergence to a distribution centered around the margin. We then present
additional empirical analyses showing that this meta-level algorithm provides a
general and effective framework that can be used in combination with a variety
of learning classifiers. We perform extensive experiments to investigate the
tradeoff achieved between scalability and accuracy, and robustness to noise, on
both synthetic and real-world data. These empirical results corroborate our
theoretical analysis, and demonstrate the potential of PSBML in achieving
scalability without sacrificing accuracy.
Nonlinear Metric Learning for kNN and SVMs through Geometric Transformations
Authors: Bibo Shi, Jundong Liu
Date: 2015-08-06 00:00:00
Tasks: Metric Learning

In recent years, research efforts to extend linear metric learning models to
handle nonlinear structures have attracted great interests. In this paper, we
propose a novel nonlinear solution through the utilization of deformable
geometric models to learn spatially varying metrics, and apply the strategy to
boost the performance of both kNN and SVM classifiers. Thin-plate splines (TPS)
are chosen as the geometric model due to their remarkable versatility and
representation power in accounting for high-order deformations. By transforming
the input space through TPS, we can pull same-class neighbors closer while
pushing different-class points farther away in kNN, as well as make the input
data points more linearly separable in SVMs. Improvements in the performance of
kNN classification are demonstrated through experiments on synthetic and real
world datasets, with comparisons made with several state-of-the-art metric
learning solutions. Our SVM-based models also achieve significant improvements
over traditional linear and kernel SVMs with the same datasets.
Hyponymy extraction of domain ontology concept based on ccrfs and hierarchy clustering
Authors: Qiang Zhan, Chunhong Wang
Date: 2015-08-06 00:00:00
Tasks: Clustering

Concept hierarchy is the backbone of ontology, and the concept hierarchy
acquisition has been a hot topic in the field of ontology learning. this paper
proposes a hyponymy extraction method of domain ontology concept based on
cascaded conditional random field(CCRFs) and hierarchy clustering. It takes
free text as extracting object, adopts CCRFs identifying the domain concepts.
First the low layer of CCRFs is used to identify simple domain concept, then
the results are sent to the high layer, in which the nesting concepts are
recognized. Next we adopt hierarchy clustering to identify the hyponymy
relation between domain ontology concepts. The experimental results demonstrate
the proposed method is efficient.
Socially Constrained Structural Learning for Groups Detection in Crowd
Authors: Francesco Solera, Simone Calderara, Rita Cucchiara
Date: 2015-08-05 00:00:00
Tasks: Clustering

Modern crowd theories agree that collective behavior is the result of the
underlying interactions among small groups of individuals. In this work, we
propose a novel algorithm for detecting social groups in crowds by means of a
Correlation Clustering procedure on people trajectories. The affinity between
crowd members is learned through an online formulation of the Structural SVM
framework and a set of specifically designed features characterizing both their
physical and social identity, inspired by Proxemic theory, Granger causality,
DTW and Heat-maps. To adhere to sociological observations, we introduce a loss
function (G-MITRE) able to deal with the complexity of evaluating group
detection performances. We show our algorithm achieves state-of-the-art results
when relying on both ground truth trajectories and tracklets previously
extracted by available detector/tracker systems.
Using Linguistic Analysis to Translate Arabic Natural Language Queries to SPARQL
Authors: Iyad AlAgha
Date: 2015-08-06 00:00:00
Tasks: Natural Language Queries, Negation

The logic-based machine-understandable framework of the Semantic Web often
challenges naive users when they try to query ontology-based knowledge bases.
Existing research efforts have approached this problem by introducing Natural
Language (NL) interfaces to ontologies. These NL interfaces have the ability to
construct SPARQL queries based on NL user queries. However, most efforts were
restricted to queries expressed in English, and they often benefited from the
advancement of English NLP tools. However, little research has been done to
support querying the Arabic content on the Semantic Web by using NL queries.
This paper presents a domain-independent approach to translate Arabic NL
queries to SPARQL by leveraging linguistic analysis. Based on a special
consideration on Noun Phrases (NPs), our approach uses a language parser to
extract NPs and the relations from Arabic parse trees and match them to the
underlying ontology. It then utilizes knowledge in the ontology to group NPs
into triple-based representations. A SPARQL query is finally generated by
extracting targets and modifiers, and interpreting them into SPARQL. The
interpretation of advanced semantic features including negation, conjunctive
and disjunctive modifiers is also supported. The approach was evaluated by
using two datasets consisting of OWL test data and queries, and the obtained
results have confirmed its feasibility to translate Arabic NL queries to
SPARQL.
Privacy-Preserving Multi-Document Summarization
Authors: Luís Marujo, José Portêlo, Wang Ling, David Martins de Matos, João P. Neto, Anatole Gershman, Jaime Carbonell, Isabel Trancoso, Bhiksha Raj
Date: 2015-08-06 00:00:00
Tasks: Document Summarization, Multi-Document Summarization, Privacy Preserving

State-of-the-art extractive multi-document summarization systems are usually
designed without any concern about privacy issues, meaning that all documents
are open to third parties. In this paper we propose a privacy-preserving
approach to multi-document summarization. Our approach enables other parties to
obtain summaries without learning anything else about the original documents'
content. We use a hashing scheme known as Secure Binary Embeddings to convert
documents representation containing key phrases and bag-of-words into bit
strings, allowing the computation of approximate distances, instead of exact
ones. Our experiments indicate that our system yields similar results to its
non-private counterpart on standard multi-document evaluation datasets.
A Gauss-Newton Method for Markov Decision Processes
Authors: Thomas Furmston, Guy Lever
Date: 2015-07-29 00:00:00

Approximate Newton methods are a standard optimization tool which aim to
maintain the benefits of Newton's method, such as a fast rate of convergence,
whilst alleviating its drawbacks, such as computationally expensive calculation
or estimation of the inverse Hessian. In this work we investigate approximate
Newton methods for policy optimization in Markov Decision Processes (MDPs). We
first analyse the structure of the Hessian of the objective function for MDPs.
We show that, like the gradient, the Hessian exhibits useful structure in the
context of MDPs and we use this analysis to motivate two Gauss-Newton Methods
for MDPs. Like the Gauss-Newton method for non-linear least squares, these
methods involve approximating the Hessian by ignoring certain terms in the
Hessian which are difficult to estimate. The approximate Hessians possess
desirable properties, such as negative definiteness, and we demonstrate several
important performance guarantees including guaranteed ascent directions,
invariance to affine transformation of the parameter space, and convergence
guarantees. We finally provide a unifying perspective of key policy search
algorithms, demonstrating that our second Gauss-Newton algorithm is closely
related to both the EM-algorithm and natural gradient ascent applied to MDPs,
but performs significantly better in practice on a range of challenging
domains.
Automatic classification of bengali sentences based on sense definitions present in bengali wordnet
Authors: Alok Ranjan Pal, Diganta Saha, Niladri Sekhar Dash
Date: 2015-08-06 00:00:00
Tasks: Classification, General Classification, text-classification, Text Classification, Word Sense Disambiguation

Based on the sense definition of words available in the Bengali WordNet, an
attempt is made to classify the Bengali sentences automatically into different
groups in accordance with their underlying senses. The input sentences are
collected from 50 different categories of the Bengali text corpus developed in
the TDIL project of the Govt. of India, while information about the different
senses of particular ambiguous lexical item is collected from Bengali WordNet.
In an experimental basis we have used Naive Bayes probabilistic model as a
useful classifier of sentences. We have applied the algorithm over 1747
sentences that contain a particular Bengali lexical item which, because of its
ambiguous nature, is able to trigger different senses that render sentences in
different meanings. In our experiment we have achieved around 84% accurate
result on the sense classification over the total input sentences. We have
analyzed those residual sentences that did not comply with our experiment and
did affect the results to note that in many cases, wrong syntactic structures
and less semantic information are the main hurdles in semantic classification
of sentences. The applicational relevance of this study is attested in
automatic text classification, machine learning, information extraction, and
word sense disambiguation.
Word sense disambiguation: a survey
Authors: Alok Ranjan Pal, Diganta Saha
Date: 2015-08-06 00:00:00
Tasks: Survey, Word Sense Disambiguation

In this paper, we made a survey on Word Sense Disambiguation (WSD). Near
about in all major languages around the world, research in WSD has been
conducted upto different extents. In this paper, we have gone through a survey
regarding the different approaches adopted in different research works, the
State of the Art in the performance in this domain, recent works in different
Indian languages and finally a survey in Bengali language. We have made a
survey on different competitions in this field and the bench mark results,
obtained from those competitions.
Fuzzy Logic Based Direct Torque Control Of Induction Motor With Space Vector Modulation
Authors: Fatih Korkmaz, İsmail Topaloğlu, Hayati Mamur
Date: 2015-08-06 00:00:00

The induction motors have wide range of applications for due to its
well-known advantages like brushless structures, low costs and robust
performances. Over the past years, many kind of control methods are proposed
for the induction motors and direct torque control has gained huge importance
inside of them due to fast dynamic torque responses and simple control
structures. However, the direct torque control method has still some handicaps
against the other control methods and most of the important of these handicaps
is high torque ripple. This paper suggests a new approach, Fuzzy logic based
space vector modulation, on the direct torque controlled induction motors and
aim of the approach is to overcome high torque ripple disadvantages of
conventional direct torque control. In order to test and compare the proposed
direct torque control method with conventional direct torque control method
simulations, in Matlab/Simulink,have been carried out in different working
conditions. The simulation results showed that a significant improvement in the
dynamic torque and speed responses when compared to the conventional direct
torque control method.
Universal Approximation of Edge Density in Large Graphs
Authors: Marc Boullé
Date: 2015-08-06 00:00:00
Tasks: Clustering, Density Estimation, Model Selection, valid

In this paper, we present a novel way to summarize the structure of large
graphs, based on non-parametric estimation of edge density in directed
multigraphs. Following coclustering approach, we use a clustering of the
vertices, with a piecewise constant estimation of the density of the edges
across the clusters, and address the problem of automatically and reliably
inferring the number of clusters, which is the granularity of the coclustering.
We use a model selection technique with data-dependent prior and obtain an
exact evaluation criterion for the posterior probability of edge density
estimation models. We demonstrate, both theoretically and empirically, that our
data-dependent modeling technique is consistent, resilient to noise, valid non
asymptotically and asymptotically behaves as an universal approximator of the
true edge density in directed multigraphs. We evaluate our method using
artificial graphs and present its practical interest on real world graphs. The
method is both robust and scalable. It is able to extract insightful patterns
in the unsupervised learning setting and to provide state of the art accuracy
when used as a preparation step for supervised learning.
Predicting Occupancy Trends in Barcelona's Bicycle Service Stations Using Open Data
Authors: Gabriel Martins Dias, Boris Bellalta, Simon Oechsner
Date: 2015-05-14 00:00:00

In 2008, the CEO of the company that manages and maintains the public bicycle
service in Barcelona recognized that one may not expect to always find a place
to leave the rented bike nearby their destination, similarly to the case when,
driving a car, people may not find a parking lot. In this work, we make
predictions about the statuses of the stations of the public bicycle service in
Barcelona. We show that it is feasible to correctly predict nearly half of the
times when the stations are either completely full of bikes or completely
empty, up to 2 days before they actually happen. That is, users might avoid
stations at times when they could not return a bicycle that they have rented
before, or when they would not find a bike to rent. To achieve that, we apply
the Random Forest algorithm to classify the status of the stations and improve
the lifetime of the models using publicly available data, such as information
about the weather forecast. Finally, we expect that the results of the
predictions can be used to improve the quality of the service and make it more
reliable for the users.
On Gobbledygook and Mood of the Philippine Senate: An Exploratory Study on the Readability and Sentiment of Selected Philippine Senators' Microposts
Authors: Fatima M. Moncada, Jaderick P. Pabico
Date: 2015-08-06 00:00:00
Tasks: Sentiment Analysis

This paper presents the findings of a readability assessment and sentiment
analysis of selected six Philippine senators' microposts over the popular
Twitter microblog. Using the Simple Measure of Gobbledygook (SMOG), tweets of
Senators Cayetano, Defensor-Santiago, Pangilinan, Marcos, Guingona, and
Escudero were assessed. A sentiment analysis was also done to determine the
polarity of the senators' respective microposts. Results showed that on the
average, the six senators are tweeting at an eight to ten SMOG level. This
means that, at least a sixth grader will be able to understand the senators'
tweets. Moreover, their tweets are mostly neutral and their sentiments vary in
unison at some period of time. This could mean that a senator's tweet sentiment
is affected by specific Philippine-based events.
The study of cuckoo optimization algorithm for production planning problem
Authors: Afsane Akbarzadeh, Elham Shadkam
Date: 2015-08-06 00:00:00

Constrained Nonlinear programming problems are hard problems, and one of the
most widely used and common problems for production planning problem to
optimize. In this study, one of the mathematical models of production planning
is survey and the problem solved by cuckoo algorithm. Cuckoo Algorithm is
efficient method to solve continues non linear problem. Moreover, mentioned
models of production planning solved with Genetic algorithm and Lingo software
and the results will compared. The Cuckoo Algorithm is suitable choice for
optimization in convergence of solution