pwc-archive/papers-with-abstracts huggingface dataset:
Statistical Analysis on E-Commerce Reviews, with Sentiment Classification using Bidirectional Recurrent Neural Network (RNN)
Authors: Abien Fred Agarap
Date: 2018-05-08 00:00:00
Tasks: Classification, General Classification, Marketing, Sentiment Analysis, Sentiment Classification

Understanding customer sentiments is of paramount importance in marketing strategies today. Not only will it give companies an insight as to how customers perceive their products and/or services, but it will also give them an idea on how to improve their offers. This paper attempts to understand the correlation of different variables in customer reviews on a women clothing e-commerce, and to classify each review whether it recommends the reviewed product or not and whether it consists of positive, negative, or neutral sentiment. To achieve these goals, we employed univariate and multivariate analyses on dataset features except for review titles and review texts, and we implemented a bidirectional recurrent neural network (RNN) with long-short term memory unit (LSTM) for recommendation and sentiment classification. Results have shown that a recommendation is a strong indicator of a positive sentiment score, and vice-versa. On the other hand, ratings in product reviews are fuzzy indicators of sentiment scores. We also found out that the bidirectional LSTM was able to reach an F1-score of 0.88 for recommendation classification, and 0.93 for sentiment classification.
A Regression Model of Recurrent Deep Neural Networks for Noise Robust Estimation of the Fundamental Frequency Contour of Speech
Authors: Akihiro Kato, Tomi Kinnunen
Date: 2018-05-08 00:00:00
Tasks: Language Identification, Speech Synthesis, Voice Conversion

The fundamental frequency (F0) contour of speech is a key aspect to represent
speech prosody that finds use in speech and spoken language analysis such as
voice conversion and speech synthesis as well as speaker and language
identification. This work proposes new methods to estimate the F0 contour of
speech using deep neural networks (DNNs) and recurrent neural networks (RNNs).
They are trained using supervised learning with the ground truth of F0
contours. The latest prior research addresses this problem first as a
frame-by-frame-classification problem followed by sequence tracking using deep
neural network hidden Markov model (DNN-HMM) hybrid architecture. This study,
however, tackles the problem as a regression problem instead, in order to
obtain F0 contours with higher frequency resolution from clean and noisy
speech. Experiments using PTDB-TUG corpus contaminated with additive noise
(NOISEX-92) show the proposed method improves gross pitch error (GPE) by more
than 25 % at signal-to-noise ratios (SNRs) between -10 dB and +10 dB as
compared with one of the most noise-robust F0 trackers, PEFAC. Furthermore, the
performance on fine pitch error (FPE) is improved by approximately 20 % against
a state-of-the-art DNN-HMM-based approach.
Conditional Variance Penalties and Domain Shift Robustness
Authors: Christina Heinze-Deml, Nicolai Meinshausen
Date: 2017-10-31 00:00:00
Tasks: General Classification, image-classification, Image Classification

When training a deep neural network for image classification, one can broadly
distinguish between two types of latent features of images that will drive the
classification. We can divide latent features into (i) "core" or "conditionally
invariant" features $X^\text{core}$ whose distribution $X^\text{core}\vert Y$,
conditional on the class $Y$, does not change substantially across domains and
(ii) "style" features $X^{\text{style}}$ whose distribution $X^{\text{style}}
\vert Y$ can change substantially across domains. Examples for style features
include position, rotation, image quality or brightness but also more complex
ones like hair color, image quality or posture for images of persons. Our goal
is to minimize a loss that is robust under changes in the distribution of these
style features. In contrast to previous work, we assume that the domain itself
is not observed and hence a latent variable.
  We do assume that we can sometimes observe a typically discrete identifier or
"$\mathrm{ID}$ variable". In some applications we know, for example, that two
images show the same person, and $\mathrm{ID}$ then refers to the identity of
the person. The proposed method requires only a small fraction of images to
have $\mathrm{ID}$ information. We group observations if they share the same
class and identifier $(Y,\mathrm{ID})=(y,\mathrm{id})$ and penalize the
conditional variance of the prediction or the loss if we condition on
$(Y,\mathrm{ID})$. Using a causal framework, this conditional variance
regularization (CoRe) is shown to protect asymptotically against shifts in the
distribution of the style variables. Empirically, we show that the CoRe penalty
improves predictive accuracy substantially in settings where domain changes
occur in terms of image quality, brightness and color while we also look at
more complex changes such as changes in movement and posture.
Comparing heterogeneous visual gestures for measuring the diversity of visual speech signals
Authors: Helen L. Bear, Richard Harvey
Date: 2018-05-08 00:00:00
Tasks: Clustering, Diversity, Lipreading

Visual lip gestures observed whilst lipreading have a few working
definitions, the most common two are; `the visual equivalent of a phoneme' and
`phonemes which are indistinguishable on the lips'. To date there is no formal
definition, in part because to date we have not established a two-way
relationship or mapping between visemes and phonemes. Some evidence suggests
that visual speech is highly dependent upon the speaker. So here, we use a
phoneme-clustering method to form new phoneme-to-viseme maps for both
individual and multiple speakers. We test these phoneme to viseme maps to
examine how similarly speakers talk visually and we use signed rank tests to
measure the distance between individuals. We conclude that broadly speaking,
speakers have the same repertoire of mouth gestures, where they differ is in
the use of the gestures.
Improving Character-level Japanese-Chinese Neural Machine Translation with Radicals as an Additional Input Feature
Authors: Jinyi Zhang, Tadahiro Matsumoto
Date: 2018-05-08 00:00:00
Tasks: Machine Translation, NMT, Translation

In recent years, Neural Machine Translation (NMT) has been proven to get
impressive results. While some additional linguistic features of input words
improve word-level NMT, any additional character features have not been used to
improve character-level NMT so far. In this paper, we show that the radicals of
Chinese characters (or kanji), as a character feature information, can be
easily provide further improvements in the character-level NMT. In experiments
on WAT2016 Japanese-Chinese scientific paper excerpt corpus (ASPEC-JP), we find
that the proposed method improves the translation quality according to two
aspects: perplexity and BLEU. The character-level NMT with the radical input
feature's model got a state-of-the-art result of 40.61 BLEU points in the test
set, which is an improvement of about 8.6 BLEU points over the best system on
the WAT2016 Japanese-to-Chinese translation subtask with ASPEC-JP. The
improvements over the character-level NMT with no additional input feature are
up to about 1.5 and 1.4 BLEU points in the development-test set and the test
set of the corpus, respectively.
The N-Tuple Bandit Evolutionary Algorithm for Game Agent Optimisation
Authors: Simon M. Lucas, Jialin Liu, Diego Perez-Liebana
Date: 2018-02-16 00:00:00

This paper describes the N-Tuple Bandit Evolutionary Algorithm (NTBEA), an
optimisation algorithm developed for noisy and expensive discrete
(combinatorial) optimisation problems. The algorithm is applied to two
game-based hyper-parameter optimisation problems. The N-Tuple system directly
models the statistics, approximating the fitness and number of evaluations of
each modelled combination of parameters. The model is simple, efficient and
informative. Results show that the NTBEA significantly outperforms grid search
and an estimation of distribution algorithm.
Phoneme-to-viseme mappings: the good, the bad, and the ugly
Authors: Helen L. Bear, Richard Harvey
Date: 2018-05-08 00:00:00

Visemes are the visual equivalent of phonemes. Although not precisely
defined, a working definition of a viseme is "a set of phonemes which have
identical appearance on the lips". Therefore a phoneme falls into one viseme
class but a viseme may represent many phonemes: a many to one mapping. This
mapping introduces ambiguity between phonemes when using viseme classifiers.
Not only is this ambiguity damaging to the performance of audio-visual
classifiers operating on real expressive speech, there is also considerable
choice between possible mappings. In this paper we explore the issue of this
choice of viseme-to-phoneme map. We show that there is definite difference in
performance between viseme-to-phoneme mappings and explore why some maps appear
to work better than others. We also devise a new algorithm for constructing
phoneme-to-viseme mappings from labeled speech data. These new visemes, `Bear'
visemes, are shown to perform better than previously known units.
Comparing phonemes and visemes with DNN-based lipreading
Authors: Kwanchiva Thangthai, Helen L. Bear, Richard Harvey
Date: 2018-05-08 00:00:00
Tasks: Decoder, Lipreading

There is debate if phoneme or viseme units are the most effective for a
lipreading system. Some studies use phoneme units even though phonemes describe
unique short sounds; other studies tried to improve lipreading accuracy by
focusing on visemes with varying results. We compare the performance of a
lipreading system by modeling visual speech using either 13 viseme or 38
phoneme units. We report the accuracy of our system at both word and unit
levels. The evaluation task is large vocabulary continuous speech using the
TCD-TIMIT corpus. We complete our visual speech modeling via hybrid DNN-HMMs
and our visual speech decoder is a Weighted Finite-State Transducer (WFST). We
use DCT and Eigenlips as a representation of mouth ROI image. The phoneme
lipreading system word accuracy outperforms the viseme based system word
accuracy. However, the phoneme system achieved lower accuracy at the unit level
which shows the importance of the dictionary for decoding classification
outputs into words.
Deep Affect Prediction in-the-wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyond
Authors: Dimitrios Kollias, Panagiotis Tzirakis, Mihalis A. Nicolaou, Athanasios Papaioannou, Guoying Zhao, Björn Schuller, Irene Kotsia, Stefanos Zafeiriou
Date: 2018-04-29 00:00:00
Tasks: Emotion Recognition

Automatic understanding of human affect using visual signals is of great
importance in everyday human-machine interactions. Appraising human emotional
states, behaviors and reactions displayed in real-world settings, can be
accomplished using latent continuous dimensions (e.g., the circumplex model of
affect). Valence (i.e., how positive or negative is an emotion) & arousal
(i.e., power of the activation of the emotion) constitute popular and effective
affect representations. Nevertheless, the majority of collected datasets this
far, although containing naturalistic emotional states, have been captured in
highly controlled recording conditions. In this paper, we introduce the
Aff-Wild benchmark for training and evaluating affect recognition algorithms.
We also report on the results of the First Affect-in-the-wild Challenge that
was organized in conjunction with CVPR 2017 on the Aff-Wild database and was
the first ever challenge on the estimation of valence and arousal in-the-wild.
Furthermore, we design and extensively train an end-to-end deep neural
architecture which performs prediction of continuous emotion dimensions based
on visual cues. The proposed deep learning architecture, AffWildNet, includes
convolutional & recurrent neural network layers, exploiting the invariant
properties of convolutional features, while also modeling temporal dynamics
that arise in human behavior via the recurrent layers. The AffWildNet produced
state-of-the-art results on the Aff-Wild Challenge. We then exploit the AffWild
database for learning features, which can be used as priors for achieving best
performances both for dimensional, as well as categorical emotion recognition,
using the RECOLA, AFEW-VA and EmotiW datasets, compared to all other methods
designed for the same goal. The database and emotion recognition models are
available at http://ibug.doc.ic.ac.uk/resources/first-affect-wild-challenge.
A Multi-component CNN-RNN Approach for Dimensional Emotion Recognition in-the-wild
Authors: Dimitrios Kollias, Stefanos Zafeiriou
Date: 2018-05-03 00:00:00
Tasks: Emotion Recognition

This paper presents our approach to the One-Minute Gradual-Emotion Recognition (OMG-Emotion) Challenge, focusing on dimensional emotion recognition through visual analysis of the provided emotion videos. The approach is based on a Convolutional and Recurrent (CNN-RNN) deep neural architecture we have developed for the relevant large AffWild Emotion Database. We extended and adapted this architecture, by letting a combination of multiple features generated in the CNN component be explored by RNN subnets. Our target has been to obtain best performance on the OMG-Emotion visual validation data set, while learning the respective visual training data set. Extended experimentation has led to best architectures for the estimation of the values of the valence and arousal emotion dimensions over these data sets.
Learning Short-Cut Connections for Object Counting
Authors: Daniel Oñoro-Rubio, Mathias Niepert, Roberto J. López-Sastre
Date: 2018-05-08 00:00:00
Tasks: Density Estimation, Object, Object Counting

Object counting is an important task in computer vision due to its growing
demand in applications such as traffic monitoring or surveillance. In this
paper, we consider object counting as a learning problem of a joint feature
extraction and pixel-wise object density estimation with
Convolutional-Deconvolutional networks. We introduce a novel counting model,
named Gated U-Net (GU-Net). Specifically, we propose to enrich the U-Net
architecture with the concept of learnable short-cut connections. Standard
short-cut connections are connections between layers in deep neural networks
which skip at least one intermediate layer. Instead of simply setting short-cut
connections, we propose to learn these connections from data. Therefore, our
short-cuts can work as gating units, which optimize the flow of information
between convolutional and deconvolutional layers in the U-Net architecture. We
evaluate the introduced GU-Net architecture on three commonly used benchmark
data sets for object counting. GU-Nets consistently outperform the base U-Net
architecture, and achieve state-of-the-art performance.
Interpretable Adversarial Perturbation in Input Embedding Space for Text
Authors: Motoki Sato, Jun Suzuki, Hiroyuki Shindo, Yuji Matsumoto
Date: 2018-05-08 00:00:00
Tasks: Sentence

Following great success in the image processing field, the idea of
adversarial training has been applied to tasks in the natural language
processing (NLP) field. One promising approach directly applies adversarial
training developed in the image processing field to the input word embedding
space instead of the discrete input space of texts. However, this approach
abandons such interpretability as generating adversarial texts to significantly
improve the performance of NLP tasks. This paper restores interpretability to
such methods by restricting the directions of perturbations toward the existing
words in the input embedding space. As a result, we can straightforwardly
reconstruct each input with perturbations to an actual text by considering the
perturbations to be the replacement of words in the sentence while maintaining
or even improving the task performance.
One "Ruler" for All Languages: Multi-Lingual Dialogue Evaluation with Adversarial Multi-Task Learning
Authors: Xiaowei Tong, Zhenxin Fu, Mingyue Shang, Dongyan Zhao, Rui Yan
Date: 2018-05-08 00:00:00
Tasks: All, Dialogue Evaluation, Multi-Task Learning

Automatic evaluating the performance of Open-domain dialogue system is a
challenging problem. Recent work in neural network-based metrics has shown
promising opportunities for automatic dialogue evaluation. However, existing
methods mainly focus on monolingual evaluation, in which the trained metric is
not flexible enough to transfer across different languages. To address this
issue, we propose an adversarial multi-task neural metric (ADVMT) for
multi-lingual dialogue evaluation, with shared feature extraction across
languages. We evaluate the proposed model in two different languages.
Experiments show that the adversarial multi-task neural metric achieves a high
correlation with human annotation, which yields better performance than
monolingual ones and various existing metrics.
The Complexity of Limited Belief Reasoning -- The Quantifier-Free Case
Authors: Yijia Chen, Abdallah Saffidine, Christoph Schwering
Date: 2018-05-08 00:00:00

The classical view of epistemic logic is that an agent knows all the logical
consequences of their knowledge base. This assumption of logical omniscience is
often unrealistic and makes reasoning computationally intractable. One approach
to avoid logical omniscience is to limit reasoning to a certain belief level,
which intuitively measures the reasoning "depth." This paper investigates the
computational complexity of reasoning with belief levels. First we show that
while reasoning remains tractable if the level is constant, the complexity
jumps to PSPACE-complete -- that is, beyond classical reasoning -- when the
belief level is part of the input. Then we further refine the picture using
parameterized complexity theory to investigate how the belief level and the
number of non-logical symbols affect the complexity.
Profitable Bandits
Authors: Mastane Achab, Stephan Clémençon, Aurélien Garivier
Date: 2018-05-08 00:00:00
Tasks: Management, Thompson Sampling

Originally motivated by default risk management applications, this paper
investigates a novel problem, referred to as the profitable bandit problem
here. At each step, an agent chooses a subset of the K possible actions. For
each action chosen, she then receives the sum of a random number of rewards.
Her objective is to maximize her cumulated earnings. We adapt and study three
well-known strategies in this purpose, that were proved to be most efficient in
other settings: kl-UCB, Bayes-UCB and Thompson Sampling. For each of them, we
prove a finite time regret bound which, together with a lower bound we obtain
as well, establishes asymptotic optimality. Our goal is also to compare these
three strategies from a theoretical and empirical perspective both at the same
time. We give simple, self-contained proofs that emphasize their similarities,
as well as their differences. While both Bayesian strategies are automatically
adapted to the geometry of information, the numerical experiments carried out
show a slight advantage for Thompson Sampling in practice.
Image Ordinal Classification and Understanding: Grid Dropout with Masking Label
Authors: Chao Zhang, Ce Zhu, Jimin Xiao, Xun Xu, Yipeng Liu
Date: 2018-05-08 00:00:00
Tasks: Age Estimation, Classification, Data Augmentation, General Classification, Ordinal Classification

Image ordinal classification refers to predicting a discrete target value
which carries ordering correlation among image categories. The limited size of
labeled ordinal data renders modern deep learning approaches easy to overfit.
To tackle this issue, neuron dropout and data augmentation were proposed which,
however, still suffer from over-parameterization and breaking spatial
structure, respectively. To address the issues, we first propose a grid dropout
method that randomly dropout/blackout some areas of the raining image. Then we
combine the objective of predicting the blackout patches with classification to
take advantage of the spatial information. Finally we demonstrate the
effectiveness of both approaches by visualizing the Class Activation Map (CAM)
and discover that grid dropout is more aware of the whole facial areas and more
robust than neuron dropout for small training dataset. Experiments are
conducted on a challenging age estimation dataset - Adience dataset with very
competitive results compared with state-of-the-art methods.
A Self-paced Regularization Framework for Partial-Label Learning
Authors: Gengyu Lyu, Songhe Feng, Congyang Lang
Date: 2018-04-20 00:00:00
Tasks: Partial Label Learning

Partial label learning (PLL) aims to solve the problem where each training
instance is associated with a set of candidate labels, one of which is the
correct label. Most PLL algorithms try to disambiguate the candidate label set,
by either simply treating each candidate label equally or iteratively
identifying the true label. Nonetheless, existing algorithms usually treat all
labels and instances equally, and the complexities of both labels and instances
are not taken into consideration during the learning stage. Inspired by the
successful application of self-paced learning strategy in machine learning
field, we integrate the self-paced regime into the partial label learning
framework and propose a novel Self-Paced Partial-Label Learning (SP-PLL)
algorithm, which could control the learning process to alleviate the problem by
ranking the priorities of the training examples together with their candidate
labels during each learning iteration. Extensive experiments and comparisons
with other baseline methods demonstrate the effectiveness and robustness of the
proposed method.
MC-GAN: Multi-conditional Generative Adversarial Network for Image Synthesis
Authors: Hyojin Park, YoungJoon Yoo, Nojun Kwak
Date: 2018-05-03 00:00:00
Tasks: Generative Adversarial Network, Image Generation, Object, Text to Image Generation, Text-to-Image Generation

In this paper, we introduce a new method for generating an object image from
text attributes on a desired location, when the base image is given. One step
further to the existing studies on text-to-image generation mainly focusing on
the object's appearance, the proposed method aims to generate an object image
preserving the given background information, which is the first attempt in this
field. To tackle the problem, we propose a multi-conditional GAN (MC-GAN) which
controls both the object and background information jointly. As a core
component of MC-GAN, we propose a synthesis block which disentangles the object
and background information in the training stage. This block enables MC-GAN to
generate a realistic object image with the desired background by controlling
the amount of the background information from the given base image using the
foreground information from the text attributes. From the experiments with
Caltech-200 bird and Oxford-102 flower datasets, we show that our model is able
to generate photo-realistic images with a resolution of 128 x 128. The source
code of MC-GAN is released.
Driving maneuvers prediction based on cognition-driven and data-driven method
Authors: Dong Zhou, Huimin Ma, Yuhan Dong
Date: 2018-05-08 00:00:00

Advanced Driver Assistance Systems (ADAS) improve driving safety
significantly. They alert drivers from unsafe traffic conditions when a
dangerous maneuver appears. Traditional methods to predict driving maneuvers
are mostly based on data-driven models alone. However, existing methods to
understand the driver's intention remain an ongoing challenge due to a lack of
intersection of human cognition and data analysis. To overcome this challenge,
we propose a novel method that combines both the cognition-driven model and the
data-driven model. We introduce a model named Cognitive Fusion-RNN (CF-RNN)
which fuses the data inside the vehicle and the data outside the vehicle in a
cognitive way. The CF-RNN model consists of two Long Short-Term Memory (LSTM)
branches regulated by human reaction time. Experiments on the Brain4Cars
benchmark dataset demonstrate that the proposed method outperforms previous
methods and achieves state-of-the-art performance.