pwc-archive/papers-with-abstracts huggingface dataset:
Fuzzy Ontology-Based Sentiment Analysis of Transportation and City Feature Reviews for Safe Traveling
Authors: Farman Ali, D. Kwak, Pervez Khan, S. M. Riazul Islam, K. H. Kim, K. S. Kwak
Date: 2017-01-19 00:00:00
Tasks: Decision Making, Sentiment Analysis

Traffic congestion is rapidly increasing in urban areas, particularly in mega
cities. To date, there exist a few sensor network based systems to address this
problem. However, these techniques are not suitable enough in terms of
monitoring an entire transportation system and delivering emergency services
when needed. These techniques require real-time data and intelligent ways to
quickly determine traffic activity from useful information. In addition, these
existing systems and websites on city transportation and travel rely on rating
scores for different factors (e.g., safety, low crime rate, cleanliness, etc.).
These rating scores are not efficient enough to deliver precise information,
whereas reviews or tweets are significant, because they help travelers and
transportation administrators to know about each aspect of the city. However,
it is difficult for travelers to read, and for transportation systems to
process, all reviews and tweets to obtain expressive sentiments regarding the
needs of the city. The optimum solution for this kind of problem is analyzing
the information available on social network platforms and performing sentiment
analysis. On the other hand, crisp ontology-based frameworks cannot extract
blurred information from tweets and reviews; therefore, they produce inadequate
results. In this regard, this paper proposes fuzzy ontology-based sentiment
analysis and SWRL rule-based decision-making to monitor transportation
activities and to make a city- feature polarity map for travelers. This system
retrieves reviews and tweets related to city features and transportation
activities. The feature opinions are extracted from these retrieved data, and
then fuzzy ontology is used to determine the transportation and city-feature
polarity. A fuzzy ontology and an intelligent system prototype are developed by
using Prot\'eg\'e OWL and Java, respectively.
Semantic Evolutionary Concept Distances for Effective Information Retrieval in Query Expansion
Authors: Valentina Franzoni, Yuanxi Li, Clement H. C. Leung, Alfredo Milani
Date: 2017-01-19 00:00:00
Tasks: Information Retrieval, Reranking, Retrieval

In this work several semantic approaches to concept-based query expansion and
reranking schemes are studied and compared with different ontology-based
expansion methods in web document search and retrieval. In particular, we focus
on concept-based query expansion schemes, where, in order to effectively
increase the precision of web document retrieval and to decrease the users
browsing time, the main goal is to quickly provide users with the most suitable
query expansion. Two key tasks for query expansion in web document retrieval
are to find the expansion candidates, as the closest concepts in web document
domain, and to rank the expanded queries properly. The approach we propose aims
at improving the expansion phase for better web document retrieval and
precision. The basic idea is to measure the distance between candidate concepts
using the PMING distance, a collaborative semantic proximity measure, i.e. a
measure which can be computed by using statistical results from web search
engine. Experiments show that the proposed technique can provide users with
more satisfying expansion results and improve the quality of web document
retrieval.
The Discrete Dantzig Selector: Estimating Sparse Linear Models via Mixed Integer Linear Optimization
Authors: Rahul Mazumder, Peter Radchenko
Date: 2015-08-08 00:00:00
Tasks: regression, Variable Selection

We propose a novel high-dimensional linear regression estimator: the Discrete
Dantzig Selector, which minimizes the number of nonzero regression coefficients
subject to a budget on the maximal absolute correlation between the features
and residuals. Motivated by the significant advances in integer optimization
over the past 10-15 years, we present a Mixed Integer Linear Optimization
(MILO) approach to obtain certifiably optimal global solutions to this
nonconvex optimization problem. The current state of algorithmics in integer
optimization makes our proposal substantially more computationally attractive
than the least squares subset selection framework based on integer quadratic
optimization, recently proposed in [8] and the continuous nonconvex quadratic
optimization framework of [33]. We propose new discrete first-order methods,
which when paired with state-of-the-art MILO solvers, lead to good solutions
for the Discrete Dantzig Selector problem for a given computational budget. We
illustrate that our integrated approach provides globally optimal solutions in
significantly shorter computation times, when compared to off-the-shelf MILO
solvers. We demonstrate both theoretically and empirically that in a wide range
of regimes the statistical properties of the Discrete Dantzig Selector are
superior to those of popular $\ell_{1}$-based approaches. We illustrate that
our approach can handle problem instances with p = 10,000 features with
certifiable optimality making it a highly scalable combinatorial variable
selection approach in sparse linear modeling.
Hierarchical Question-Image Co-Attention for Visual Question Answering
Authors: Jiasen Lu, Jianwei Yang, Dhruv Batra, Devi Parikh
Date: 2016-05-31 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Visual Dialog, Visual Question Answering, Visual Question Answering (VQA)

A number of recent works have proposed attention models for Visual Question
Answering (VQA) that generate spatial maps highlighting image regions relevant
to answering the question. In this paper, we argue that in addition to modeling
"where to look" or visual attention, it is equally important to model "what
words to listen to" or question attention. We present a novel co-attention
model for VQA that jointly reasons about image and question attention. In
addition, our model reasons about the question (and consequently the image via
the co-attention mechanism) in a hierarchical fashion via a novel 1-dimensional
convolution neural networks (CNN). Our model improves the state-of-the-art on
the VQA dataset from 60.3% to 60.5%, and from 61.6% to 63.3% on the COCO-QA
dataset. By using ResNet, the performance is further improved to 62.1% for VQA
and 65.4% for COCO-QA.
Heterogeneous Information Network Embedding for Meta Path based Proximity
Authors: Zhipeng Huang, Nikos Mamoulis
Date: 2017-01-19 00:00:00
Tasks: Network Embedding

A network embedding is a representation of a large graph in a low-dimensional
space, where vertices are modeled as vectors. The objective of a good embedding
is to preserve the proximity between vertices in the original graph. This way,
typical search and mining methods can be applied in the embedded space with the
help of off-the-shelf multidimensional indexing approaches. Existing network
embedding techniques focus on homogeneous networks, where all vertices are
considered to belong to a single class.
Accurate Motion Estimation through Random Sample Aggregated Consensus
Authors: Martin Rais, Gabriele Facciolo, Enric Meinhardt-Llopis, Jean-Michel Morel, Antoni Buades, Bartomeu Coll
Date: 2017-01-19 00:00:00
Tasks: Motion Estimation

We reconsider the classic problem of estimating accurately a 2D
transformation from point matches between images containing outliers. RANSAC
discriminates outliers by randomly generating minimalistic sampled hypotheses
and verifying their consensus over the input data. Its response is based on the
single hypothesis that obtained the largest inlier support. In this article we
show that the resulting accuracy can be improved by aggregating all generated
hypotheses. This yields RANSAAC, a framework that improves systematically over
RANSAC and its state-of-the-art variants by statistically aggregating
hypotheses. To this end, we introduce a simple strategy that allows to rapidly
average 2D transformations, leading to an almost negligible extra computational
cost. We give practical applications on projective transforms and
homography+distortion models and demonstrate a significant performance gain in
both cases.
Online Structure Learning for Sum-Product Networks with Gaussian Leaves
Authors: Wilson Hsu, Agastya Kalra, Pascal Poupart
Date: 2017-01-19 00:00:00
Tasks: valid

Sum-product networks have recently emerged as an attractive representation
due to their dual view as a special type of deep neural network with clear
semantics and a special type of probabilistic graphical model for which
inference is always tractable. Those properties follow from some conditions
(i.e., completeness and decomposability) that must be respected by the
structure of the network. As a result, it is not easy to specify a valid
sum-product network by hand and therefore structure learning techniques are
typically used in practice. This paper describes the first online structure
learning technique for continuous SPNs with Gaussian leaves. We also introduce
an accompanying new parameter learning technique.
On the Usability of Probably Approximately Correct Implication Bases
Authors: Daniel Borchmann, Tom Hanika, Sergei Obiedkov
Date: 2017-01-04 00:00:00

We revisit the notion of probably approximately correct implication bases
from the literature and present a first formulation in the language of formal
concept analysis, with the goal to investigate whether such bases represent a
suitable substitute for exact implication bases in practical use-cases. To this
end, we quantitatively examine the behavior of probably approximately correct
implication bases on artificial and real-world data sets and compare their
precision and recall with respect to their corresponding exact implication
bases. Using a small example, we also provide qualitative insight that
implications from probably approximately correct bases can still represent
meaningful knowledge from a given data set.
Deep Neural Networks for Czech Multi-label Document Classification
Authors: Ladislav Lenc, Pavel Král
Date: 2017-01-13 00:00:00
Tasks: Classification, Document Classification, General Classification

This paper is focused on automatic multi-label document classification of Czech text documents. The current approaches usually use some pre-processing which can have negative impact (loss of information, additional implementation work, etc). Therefore, we would like to omit it and use deep neural networks that learn from simple features. This choice was motivated by their successful usage in many other machine learning fields. Two different networks are compared: the first one is a standard multi-layer perceptron, while the second one is a popular convolutional network. The experiments on a Czech newspaper corpus show that both networks significantly outperform baseline method which uses a rich set of features with maximum entropy classifier. We have also shown that convolutional network gives the best results.
Lipschitz Properties for Deep Convolutional Networks
Authors: Radu Balan, Maneesh Singh, Dongmian Zou
Date: 2017-01-18 00:00:00
Tasks: General Classification

In this paper we discuss the stability properties of convolutional neural
networks. Convolutional neural networks are widely used in machine learning. In
classification they are mainly used as feature extractors. Ideally, we expect
similar features when the inputs are from the same class. That is, we hope to
see a small change in the feature vector with respect to a deformation on the
input signal. This can be established mathematically, and the key step is to
derive the Lipschitz properties. Further, we establish that the stability
results can be extended for more general networks. We give a formula for
computing the Lipschitz bound, and compare it with other methods to show it is
closer to the optimal value.
Connecting Generative Adversarial Networks and Actor-Critic Methods
Authors: David Pfau, Oriol Vinyals
Date: 2016-10-06 00:00:00
Tasks: Reinforcement Learning, Reinforcement Learning (RL)

Both generative adversarial networks (GAN) in unsupervised learning and
actor-critic methods in reinforcement learning (RL) have gained a reputation
for being difficult to optimize. Practitioners in both fields have amassed a
large number of strategies to mitigate these instabilities and improve
training. Here we show that GANs can be viewed as actor-critic methods in an
environment where the actor cannot affect the reward. We review the strategies
for stabilizing training for each class of models, both those that generalize
between the two and those that are particular to that model. We also review a
number of extensions to GANs and RL algorithms with even more complicated
information flow. We hope that by highlighting this formal connection we will
encourage both GAN and RL communities to develop general, scalable, and stable
algorithms for multilevel optimization with deep networks, and to draw
inspiration across communities.
ERBlox: Combining Matching Dependencies with Machine Learning for Entity Resolution
Authors: Zeinab Bahmani, Leopoldo Bertossi, Nikolaos Vasiloglou
Date: 2016-02-07 00:00:00
Tasks: Attribute, BIG-bench Machine Learning, Blocking, Entity Resolution

Entity resolution (ER), an important and common data cleaning problem, is
about detecting data duplicate representations for the same external entities,
and merging them into single representations. Relatively recently, declarative
rules called "matching dependencies" (MDs) have been proposed for specifying
similarity conditions under which attribute values in database records are
merged. In this work we show the process and the benefits of integrating four
components of ER: (a) Building a classifier for duplicate/non-duplicate record
pairs built using machine learning (ML) techniques; (b) Use of MDs for
supporting the blocking phase of ML; (c) Record merging on the basis of the
classifier results; and (d) The use of the declarative language "LogiQL" -an
extended form of Datalog supported by the "LogicBlox" platform- for all
activities related to data processing, and the specification and enforcement of
MDs.
Temporal Overdrive Recurrent Neural Network
Authors: Filippo Maria Bianchi, Michael Kampffmeyer, Enrico Maiorino, Robert Jenssen
Date: 2017-01-18 00:00:00
Tasks: Time Series, Time Series Analysis, Time Series Prediction

In this work we present a novel recurrent neural network architecture
designed to model systems characterized by multiple characteristic timescales
in their dynamics. The proposed network is composed by several recurrent groups
of neurons that are trained to separately adapt to each timescale, in order to
improve the system identification process. We test our framework on time series
prediction tasks and we show some promising, preliminary results achieved on
synthetic data. To evaluate the capabilities of our network, we compare the
performance with several state-of-the-art recurrent architectures.
Deep Learning Features at Scale for Visual Place Recognition
Authors: Zetao Chen, Adam Jacobson, Niko Sunderhauf, Ben Upcroft, Lingqiao Liu, Chunhua Shen, Ian Reid, Michael Milford
Date: 2017-01-18 00:00:00
Tasks: Deep Learning, Visual Place Recognition

The success of deep learning techniques in the computer vision domain has
triggered a range of initial investigations into their utility for visual place
recognition, all using generic features from networks that were trained for
other types of recognition tasks. In this paper, we train, at large scale, two
CNN architectures for the specific place recognition task and employ a
multi-scale feature encoding method to generate condition- and
viewpoint-invariant features. To enable this training to occur, we have
developed a massive Specific PlacEs Dataset (SPED) with hundreds of examples of
place appearance change at thousands of different places, as opposed to the
semantic place type datasets currently available. This new dataset enables us
to set up a training regime that interprets place recognition as a
classification problem. We comprehensively evaluate our trained networks on
several challenging benchmark place recognition datasets and demonstrate that
they achieve an average 10% increase in performance over other place
recognition algorithms and pre-trained CNNs. By analyzing the network responses
and their differences from pre-trained networks, we provide insights into what
a network learns when training for place recognition, and what these results
signify for future research in this area.
On the Performance of Network Parallel Training in Artificial Neural Networks
Authors: Ludvig Ericson, Rendani Mbuvha
Date: 2017-01-18 00:00:00

Artificial Neural Networks (ANNs) have received increasing attention in
recent years with applications that span a wide range of disciplines including
vital domains such as medicine, network security and autonomous transportation.
However, neural network architectures are becoming increasingly complex and
with an increasing need to obtain real-time results from such models, it has
become pivotal to use parallelization as a mechanism for speeding up network
training and deployment. In this work we propose an implementation of Network
Parallel Training through Cannon's Algorithm for matrix multiplication. We show
that increasing the number of processes speeds up training until the point
where process communication costs become prohibitive; this point varies by
network complexity. We also show through empirical efficiency calculations that
the speedup obtained is superlinear.
NMODE --- Neuro-MODule Evolution
Authors: Keyan Ghazi-Zahedi
Date: 2017-01-18 00:00:00

Modularisation, repetition, and symmetry are structural features shared by
almost all biological neural networks. These features are very unlikely to be
found by the means of structural evolution of artificial neural networks. This
paper introduces NMODE, which is specifically designed to operate on
neuro-modules. NMODE addresses a second problem in the context of evolutionary
robotics, which is incremental evolution of complex behaviours for complex
machines, by offering a way to interface neuro-modules. The scenario in mind is
a complex walking machine, for which a locomotion module is evolved first, that
is then extended by other modules in later stages. We show that NMODE is able
to evolve a locomotion behaviour for a standard six-legged walking machine in
approximately 10 generations and show how it can be used for incremental
evolution of a complex walking machine. The entire source code used in this
paper is publicly available through GitHub.
Ontology based system to guide internship assignment process
Authors: Abir M 'Baya, Jannik Laval, Nejib Moalla, Yacine Ouzrout, Abdelaziz Bouras
Date: 2017-01-18 00:00:00
Tasks: Decision Making

Internship assignment is a complicated process for universities since it is
necessary to take into account a multiplicity of variables to establish a
compromise between companies' requirements and student competencies acquired
during the university training. These variables build up a complex relations
map that requires the formulation of an exhaustive and rigorous conceptual
scheme. In this research a domain ontological model is presented as support to
the student's decision making for opportunities of University studies level of
the University Lumiere Lyon 2 (ULL) education system. The ontology is designed
and created using methodological approach offering the possibility of improving
the progressive creation, capture and knowledge articulation. In this paper, we
draw a balance taking the demands of the companies across the capabilities of
the students. This will be done through the establishment of an ontological
model of an educational learners' profile and the internship postings which are
written in a free text and using uncontrolled vocabulary. Furthermore, we
outline the process of semantic matching which improves the quality of query
results.
Highly Efficient Hierarchical Online Nonlinear Regression Using Second Order Methods
Authors: Burak C. Civek, Ibrahim Delibalta, Suleyman S. Kozat
Date: 2017-01-18 00:00:00
Tasks: regression, Second-order methods

We introduce highly efficient online nonlinear regression algorithms that are
suitable for real life applications. We process the data in a truly online
manner such that no storage is needed, i.e., the data is discarded after being
used. For nonlinear modeling we use a hierarchical piecewise linear approach
based on the notion of decision trees where the space of the regressor vectors
is adaptively partitioned based on the performance. As the first time in the
literature, we learn both the piecewise linear partitioning of the regressor
space as well as the linear models in each region using highly effective second
order methods, i.e., Newton-Raphson Methods. Hence, we avoid the well known
over fitting issues by using piecewise linear models, however, since both the
region boundaries as well as the linear models in each region are trained using
the second order methods, we achieve substantial performance compared to the
state of the art. We demonstrate our gains over the well known benchmark data
sets and provide performance results in an individual sequence manner
guaranteed to hold without any statistical assumptions. Hence, the introduced
algorithms address computational complexity issues widely encountered in real
life applications while providing superior guaranteed performance in a strong
deterministic sense.
Assessing User Expertise in Spoken Dialog System Interactions
Authors: Eugénio Ribeiro, Fernando Batista, Isabel Trancoso, José Lopes, Ricardo Ribeiro, David Martins de Matos
Date: 2017-01-18 00:00:00

Identifying the level of expertise of its users is important for a system
since it can lead to a better interaction through adaptation techniques.
Furthermore, this information can be used in offline processes of root cause
analysis. However, not much effort has been put into automatically identifying
the level of expertise of an user, especially in dialog-based interactions. In
this paper we present an approach based on a specific set of task related
features. Based on the distribution of the features among the two classes -
Novice and Expert - we used Random Forests as a classification approach.
Furthermore, we used a Support Vector Machine classifier, in order to perform a
result comparison. By applying these approaches on data from a real system,
Let's Go, we obtained preliminary results that we consider positive, given the
difficulty of the task and the lack of competing approaches for comparison.
Converting Cascade-Correlation Neural Nets into Probabilistic Generative Models
Authors: Ardavan Salehi Nobandegani, Thomas R. Shultz
Date: 2017-01-18 00:00:00

Humans are not only adept in recognizing what class an input instance belongs
to (i.e., classification task), but perhaps more remarkably, they can imagine
(i.e., generate) plausible instances of a desired class with ease, when
prompted. Inspired by this, we propose a framework which allows transforming
Cascade-Correlation Neural Networks (CCNNs) into probabilistic generative
models, thereby enabling CCNNs to generate samples from a category of interest.
CCNNs are a well-known class of deterministic, discriminative NNs, which
autonomously construct their topology, and have been successful in giving
accounts for a variety of psychological phenomena. Our proposed framework is
based on a Markov Chain Monte Carlo (MCMC) method, called the
Metropolis-adjusted Langevin algorithm, which capitalizes on the gradient
information of the target distribution to direct its explorations towards
regions of high probability, thereby achieving good mixing properties. Through
extensive simulations, we demonstrate the efficacy of our proposed framework.