pwc-archive/papers-with-abstracts huggingface dataset:
Looking Beyond Appearances: Synthetic Training Data for Deep CNNs in Re-identification
Authors: Igor Barros Barbosa, Marco Cristani, Barbara Caputo, Aleksander Rognhaugen, Theoharis Theoharis
Date: 2017-01-11 00:00:00

Re-identification is generally carried out by encoding the appearance of a
subject in terms of outfit, suggesting scenarios where people do not change
their attire. In this paper we overcome this restriction, by proposing a
framework based on a deep convolutional neural network, SOMAnet, that
additionally models other discriminative aspects, namely, structural attributes
of the human figure (e.g. height, obesity, gender). Our method is unique in
many respects. First, SOMAnet is based on the Inception architecture, departing
from the usual siamese framework. This spares expensive data preparation
(pairing images across cameras) and allows the understanding of what the
network learned. Second, and most notably, the training data consists of a
synthetic 100K instance dataset, SOMAset, created by photorealistic human body
generation software. Synthetic data represents a good compromise between
realistic imagery, usually not required in re-identification since surveillance
cameras capture low-resolution silhouettes, and complete control of the
samples, which is useful in order to customize the data w.r.t. the surveillance
scenario at-hand, e.g. ethnicity. SOMAnet, trained on SOMAset and fine-tuned on
recent re-identification benchmarks, outperforms all competitors, matching
subjects even with different apparel. The combination of synthetic data with
Inception architectures opens up new research avenues in re-identification.
Hierarchical Multi-resolution Mesh Networks for Brain Decoding
Authors: Itir Onal Ertugrul, Mete Ozay, Fatos Tunay Yarman Vural
Date: 2016-07-12 00:00:00
Tasks: ARC, Brain Decoding, Functional Connectivity, Time Series Analysis

We propose a new framework, called Hierarchical Multi-resolution Mesh
Networks (HMMNs), which establishes a set of brain networks at multiple time
resolutions of fMRI signal to represent the underlying cognitive process. The
suggested framework, first, decomposes the fMRI signal into various frequency
subbands using wavelet transforms. Then, a brain network, called mesh network,
is formed at each subband by ensembling a set of local meshes. The locality
around each anatomic region is defined with respect to a neighborhood system
based on functional connectivity. The arc weights of a mesh are estimated by
ridge regression formed among the average region time series. In the final
step, the adjacency matrices of mesh networks obtained at different subbands
are ensembled for brain decoding under a hierarchical learning architecture,
called, fuzzy stacked generalization (FSG). Our results on Human Connectome
Project task-fMRI dataset reflect that the suggested HMMN model can
successfully discriminate tasks by extracting complementary information
obtained from mesh arc weights of multiple subbands. We study the topological
properties of the mesh networks at different resolutions using the network
measures, namely, node degree, node strength, betweenness centrality and global
efficiency; and investigate the connectivity of anatomic regions, during a
cognitive task. We observe significant variations among the network topologies
obtained for different subbands. We, also, analyze the diversity properties of
classifier ensemble, trained by the mesh networks in multiple subbands and
observe that the classifiers in the ensemble collaborate with each other to
fuse the complementary information freed at each subband. We conclude that the
fMRI data, recorded during a cognitive task, embed diverse information across
the anatomic regions at each resolution.
Guaranteed Parameter Estimation for Discrete Energy Minimization
Authors: Mengtian Li, Daniel Huber
Date: 2017-01-11 00:00:00
Tasks: parameter estimation, Scene Parsing

Structural learning, a method to estimate the parameters for discrete energy
minimization, has been proven to be effective in solving computer vision
problems, especially in 3D scene parsing. As the complexity of the models
increases, structural learning algorithms turn to approximate inference to
retain tractability. Unfortunately, such methods often fail because the
approximation can be arbitrarily poor. In this work, we propose a method to
overcome this limitation through exploiting the properties of the joint problem
of training time inference and learning. With the help of the learning
framework, we transform the inapproximable inference problem into a polynomial
time solvable one, thereby enabling tractable exact inference while still
allowing an arbitrary graph structure and full potential interactions. Our
learning algorithm is guaranteed to return a solution with a bounded error to
the global optimal within the feasible parameter space. We demonstrate the
effectiveness of this method on two point cloud scene parsing datasets. Our
approach runs much faster and solves a problem that is intractable for
previous, well-known approaches.
Deep Recurrent Q-Learning for Partially Observable MDPs
Authors: Matthew Hausknecht, Peter Stone
Date: 2015-07-23 00:00:00
Tasks: Atari Games, Deep Reinforcement Learning, OpenAI Gym, Q-Learning, Reinforcement Learning

Deep Reinforcement Learning has yielded proficient controllers for complex
tasks. However, these controllers have limited memory and rely on being able to
perceive the complete game screen at each decision point. To address these
shortcomings, this article investigates the effects of adding recurrency to a
Deep Q-Network (DQN) by replacing the first post-convolutional fully-connected
layer with a recurrent LSTM. The resulting \textit{Deep Recurrent Q-Network}
(DRQN), although capable of seeing only a single frame at each timestep,
successfully integrates information through time and replicates DQN's
performance on standard Atari games and partially observed equivalents
featuring flickering game screens. Additionally, when trained with partial
observations and evaluated with incrementally more complete observations,
DRQN's performance scales as a function of observability. Conversely, when
trained with full observations and evaluated with partial observations, DRQN's
performance degrades less than DQN's. Thus, given the same length of history,
recurrency is a viable alternative to stacking a history of frames in the DQN's
input layer and while recurrency confers no systematic advantage when learning
to play the game, the recurrent net can better adapt at evaluation time if the
quality of observations changes.
De-identification In practice
Authors: Besat Kassaie
Date: 2017-01-11 00:00:00
Tasks: De-identification

We report our effort to identify the sensitive information, subset of data
items listed by HIPAA (Health Insurance Portability and Accountability), from
medical text using the recent advances in natural language processing and
machine learning techniques. We represent the words with high dimensional
continuous vectors learned by a variant of Word2Vec called Continous Bag Of
Words (CBOW). We feed the word vectors into a simple neural network with a Long
Short-Term Memory (LSTM) architecture. Without any attempts to extract manually
crafted features and considering that our medical dataset is too small to be
fed into neural network, we obtained promising results. The results thrilled us
to think about the larger scale of the project with precise parameter tuning
and other possible improvements.
Single Image Restoration for Participating Media Based on Prior Fusion
Authors: Joel D. O. Gaya, Felipe Codevilla, Amanda C. Duarte, Paulo L. Drews-Jr, Silvia S. Botelho
Date: 2016-03-06 00:00:00
Tasks: Image Restoration, Sand

This paper describes a method to restore degraded images captured in a
participating media -- fog, turbid water, sand storm, etc. Differently from the
related work that only deal with a medium, we obtain generality by using an
image formation model and a fusion of new image priors. The model considers the
image color variation produced by the medium. The proposed restoration method
is based on the fusion of these priors and supported by statistics collected on
images acquired in both non-participating and participating media. The key of
the method is to fuse two complementary measures --- local contrast and color
data. The obtained results on underwater and foggy images demonstrate the
capabilities of the proposed method. Moreover, we evaluated our method using a
special dataset for which a ground-truth image is available.
Job Detection in Twitter
Authors: Besat Kassaie
Date: 2017-01-11 00:00:00

In this report, we propose a new application for twitter data called
\textit{job detection}. We identify people's job category based on their
tweets. As a preliminary work, we limited our task to identify only IT workers
from other job holders. We have used and compared both simple bag of words
model and a document representation based on Skip-gram model. Our results show
that the model based on Skip-gram, achieves a 76\% precision and 82\% recall.
Error Metrics for Learning Reliable Manifolds from Streaming Data
Authors: Frank Schoeneman, Suchismit Mahapatra, Varun Chandola, Nils Napp, Jaroslaw Zola
Date: 2016-11-13 00:00:00
Tasks: Dimensionality Reduction

Spectral dimensionality reduction is frequently used to identify
low-dimensional structure in high-dimensional data. However, learning
manifolds, especially from the streaming data, is computationally and memory
expensive. In this paper, we argue that a stable manifold can be learned using
only a fraction of the stream, and the remaining stream can be mapped to the
manifold in a significantly less costly manner. Identifying the transition
point at which the manifold is stable is the key step. We present error metrics
that allow us to identify the transition point for a given stream by
quantitatively assessing the quality of a manifold learned using Isomap. We
further propose an efficient mapping algorithm, called S-Isomap, that can be
used to map new samples onto the stable manifold. We describe experiments on a
variety of data sets that show that the proposed approach is computationally
efficient without sacrificing accuracy.
Efficient Twitter Sentiment Classification using Subjective Distant Supervision
Authors: Tapan Sahni, Chinmay Chandak, Naveen Reddy Chedeti, Manish Singh
Date: 2017-01-11 00:00:00
Tasks: Classification, General Classification, Sentiment Analysis, Sentiment Classification, Twitter Sentiment Analysis

As microblogging services like Twitter are becoming more and more influential
in today's globalised world, its facets like sentiment analysis are being
extensively studied. We are no longer constrained by our own opinion. Others
opinions and sentiments play a huge role in shaping our perspective. In this
paper, we build on previous works on Twitter sentiment analysis using Distant
Supervision. The existing approach requires huge computation resource for
analysing large number of tweets. In this paper, we propose techniques to speed
up the computation process for sentiment analysis. We use tweet subjectivity to
select the right training samples. We also introduce the concept of EFWS
(Effective Word Score) of a tweet that is derived from polarity scores of
frequently used words, which is an additional heuristic that can be used to
speed up the sentiment classification with standard machine learning
algorithms. We performed our experiments using 1.6 million tweets. Experimental
evaluations show that our proposed technique is more efficient and has higher
accuracy compared to previously proposed methods. We achieve overall accuracies
of around 80% (EFWS heuristic gives an accuracy around 85%) on a training
dataset of 100K tweets, which is half the size of the dataset used for the
baseline model. The accuracy of our proposed model is 2-3% higher than the
baseline model, and the model effectively trains at twice the speed of the
baseline model.
Linear Disentangled Representation Learning for Facial Actions
Authors: Xiang Xiang, Trac. D. Tran
Date: 2017-01-11 00:00:00
Tasks: Facial Action Unit Detection, Representation Learning, Sparse Representation-based Classification

Limited annotated data available for the recognition of facial expression and
action units embarrasses the training of deep networks, which can learn
disentangled invariant features. However, a linear model with just several
parameters normally is not demanding in terms of training data. In this paper,
we propose an elegant linear model to untangle confounding factors in
challenging realistic multichannel signals such as 2D face videos. The simple
yet powerful model does not rely on huge training data and is natural for
recognizing facial actions without explicitly disentangling the identity. Base
on well-understood intuitive linear models such as Sparse Representation based
Classification (SRC), previous attempts require a prepossessing of explicit
decoupling which is practically inexact. Instead, we exploit the low-rank
property across frames to subtract the underlying neutral faces which are
modeled jointly with sparse representation on the action components with group
sparsity enforced. On the extended Cohn-Kanade dataset (CK+), our one-shot
automatic method on raw face videos performs as competitive as SRC applied on
manually prepared action components and performs even better than SRC in terms
of true positive rate. We apply the model to the even more challenging task of
facial action unit recognition, verified on the MPI Face Video Database
(MPI-VDB) achieving a decent performance. All the programs and data have been
made publicly available.
Modeling Grasp Motor Imagery through Deep Conditional Generative Models
Authors: Matthew Veres, Medhat Moussa, Graham W. Taylor
Date: 2017-01-11 00:00:00
Tasks: Motor Imagery, Object

Grasping is a complex process involving knowledge of the object, the
surroundings, and of oneself. While humans are able to integrate and process
all of the sensory information required for performing this task, equipping
machines with this capability is an extremely challenging endeavor. In this
paper, we investigate how deep learning techniques can allow us to translate
high-level concepts such as motor imagery to the problem of robotic grasp
synthesis. We explore a paradigm based on generative models for learning
integrated object-action representations, and demonstrate its capacity for
capturing and generating multimodal, multi-finger grasp configurations on a
simulated grasping dataset.
Compressive Sensing via Convolutional Factor Analysis
Authors: Xin Yuan, Yunchen Pu, Lawrence Carin
Date: 2017-01-11 00:00:00
Tasks: Compressive Sensing, General Classification

We solve the compressive sensing problem via convolutional factor analysis,
where the convolutional dictionaries are learned {\em in situ} from the
compressed measurements. An alternating direction method of multipliers (ADMM)
paradigm for compressive sensing inversion based on convolutional factor
analysis is developed. The proposed algorithm provides reconstructed images as
well as features, which can be directly used for recognition ($e.g.$,
classification) tasks. When a deep (multilayer) model is constructed, a
stochastic unpooling process is employed to build a generative model. During
reconstruction and testing, we project the upper layer dictionary to the data
level and only a single layer deconvolution is required. We demonstrate that
using $\sim30\%$ (relative to pixel numbers) compressed measurements, the
proposed model achieves the classification accuracy comparable to the original
data on MNIST. We also observe that when the compressed measurements are very
limited ($e.g.$, $<10\%$), the upper layer dictionary can provide better
reconstruction results than the bottom layer.
A Framework for Knowledge Management and Automated Reasoning Applied on Intelligent Transport Systems
Authors: Aneta Vulgarakis Feljan, Athanasios Karapantelakis, Leonid Mokrushin, Hongxin Liang, Rafia Inam, Elena Fersman, Carlos R. B. Azevedo, Klaus Raizer, Ricardo S. Souza
Date: 2017-01-11 00:00:00
Tasks: Management

Cyber-Physical Systems in general, and Intelligent Transport Systems (ITS) in
particular use heterogeneous data sources combined with problem solving
expertise in order to make critical decisions that may lead to some form of
actions e.g., driver notifications, change of traffic light signals and braking
to prevent an accident. Currently, a major part of the decision process is done
by human domain experts, which is time-consuming, tedious and error-prone.
Additionally, due to the intrinsic nature of knowledge possession this decision
process cannot be easily replicated or reused. Therefore, there is a need for
automating the reasoning processes by providing computational systems a formal
representation of the domain knowledge and a set of methods to process that
knowledge. In this paper, we propose a knowledge model that can be used to
express both declarative knowledge about the systems' components, their
relations and their current state, as well as procedural knowledge representing
possible system behavior. In addition, we introduce a framework for knowledge
management and automated reasoning (KMARF). The idea behind KMARF is to
automatically select an appropriate problem solver based on formalized
reasoning expertise in the knowledge base, and convert a problem definition to
the corresponding format. This approach automates reasoning, thus reducing
operational costs, and enables reusability of knowledge and methods across
different domains. We illustrate the approach on a transportation planning use
case.
A Large Dimensional Analysis of Least Squares Support Vector Machines
Authors: Zhenyu Liao, Romain Couillet
Date: 2017-01-11 00:00:00

In this article, a large dimensional performance analysis of kernel least
squares support vector machines (LS-SVMs) is provided under the assumption of a
two-class Gaussian mixture model for the input data. Building upon recent
advances in random matrix theory, we show, when the dimension of data $p$ and
their number $n$ are both large, that the LS-SVM decision function can be well
approximated by a normally distributed random variable, the mean and variance
of which depend explicitly on a local behavior of the kernel function. This
theoretical result is then applied to the MNIST and Fashion-MNIST datasets
which, despite their non-Gaussianity, exhibit a convincingly close behavior.
Most importantly, our analysis provides a deeper understanding of the mechanism
into play in SVM-type methods and in particular of the impact on the choice of
the kernel function as well as some of their theoretical limits in separating
high dimensional Gaussian vectors.
Distinguishing Antonyms and Synonyms in a Pattern-based Neural Network
Authors: Kim Anh Nguyen, Sabine Schulte im Walde, Ngoc Thang Vu
Date: 2017-01-11 00:00:00
Proceeding: EACL 2017 4
Tasks: General Classification

Distinguishing between antonyms and synonyms is a key task to achieve high
performance in NLP systems. While they are notoriously difficult to distinguish
by distributional co-occurrence models, pattern-based methods have proven
effective to differentiate between the relations. In this paper, we present a
novel neural network model AntSynNET that exploits lexico-syntactic patterns
from syntactic parse trees. In addition to the lexical and syntactic
information, we successfully integrate the distance between the related words
along the syntactic path as a new pattern feature. The results from
classification experiments show that AntSynNET improves the performance over
prior pattern-based methods.
Cross-lingual RST Discourse Parsing
Authors: Chloé Braud, Maximin Coavoux, Anders Søgaard
Date: 2017-01-11 00:00:00
Proceeding: EACL 2017 4
Tasks: Discourse Parsing

Discourse parsing is an integral part of understanding information flow and
argumentative structure in documents. Most previous research has focused on
inducing and evaluating models from the English RST Discourse Treebank.
However, discourse treebanks for other languages exist, including Spanish,
German, Basque, Dutch and Brazilian Portuguese. The treebanks share the same
underlying linguistic theory, but differ slightly in the way documents are
annotated. In this paper, we present (a) a new discourse parser which is
simpler, yet competitive (significantly better on 2/3 metrics) to state of the
art for English, (b) a harmonization of discourse treebanks across languages,
enabling us to present (c) what to the best of our knowledge are the first
experiments on cross-lingual discourse parsing.
Question Analysis for Arabic Question Answering Systems
Authors: Waheeb Ahmed, Dr. Anto P Babu
Date: 2017-01-11 00:00:00
Tasks: POS, Question Answering

The first step of processing a question in Question Answering(QA) Systems is
to carry out a detailed analysis of the question for the purpose of determining
what it is asking for and how to perfectly approach answering it. Our Question
analysis uses several techniques to analyze any question given in natural
language: a Stanford POS Tagger & parser for Arabic language, a named entity
recognizer, tokenizer,Stop-word removal, Question expansion, Question
classification and Question focus extraction components. We employ numerous
detection rules and trained classifier using features from this analysis to
detect important elements of the question, including: 1) the portion of the
question that is a referring to the answer (the focus); 2) different terms in
the question that identify what type of entity is being asked for (the lexical
answer types); 3) Question expansion ; 4) a process of classifying the question
into one or more of several and different types; and We describe how these
elements are identified and evaluate the effect of accurate detection on our
question-answering system using the Mean Reciprocal Rank(MRR) accuracy measure.
A statistical model of tristimulus measurements within and between OLED displays
Authors: Matti Raitoharju, Samu Kallio, Matti Pellikka
Date: 2016-08-28 00:00:00

We present an empirical model for noises in color measurements from OLED
displays. According to measured data the noise is not isotropic in the XYZ
space, instead most of the noise is along an axis that is parallel to a vector
from origin to measured XYZ vector. The presented empirical model is simple and
depends only on the measured XYZ values. Our tests show that the variations
between multiple panels of the same type have similar distribution as the
temporal noise in measurements from a single panel, but a larger magnitude.
A Multifaceted Evaluation of Neural versus Phrase-Based Machine Translation for 9 Language Directions
Authors: Antonio Toral, Víctor M. Sánchez-Cartagena
Date: 2017-01-11 00:00:00
Proceeding: EACL 2017 4
Tasks: Machine Translation, Sentence, Translation

We aim to shed light on the strengths and weaknesses of the newly introduced
neural machine translation paradigm. To that end, we conduct a multifaceted
evaluation in which we compare outputs produced by state-of-the-art neural
machine translation and phrase-based machine translation systems for 9 language
directions across a number of dimensions. Specifically, we measure the
similarity of the outputs, their fluency and amount of reordering, the effect
of sentence length and performance across different error categories. We find
out that translations produced by neural machine translation systems are
considerably different, more fluent and more accurate in terms of word order
compared to those produced by phrase-based systems. Neural machine translation
systems are also more accurate at producing inflected forms, but they perform
poorly when translating very long sentences.