pwc-archive/papers-with-abstracts huggingface dataset:
Question-Aware Sentence Gating Networks for Question and Answering
Authors: Minjeong Kim, David Keetae Park, Hyungjong Noh, Yeonsoo Lee, Jaegul Choo
Date: 2018-07-20 00:00:00
Tasks: Question Answering, Reading Comprehension, Sentence

Machine comprehension question answering, which finds an answer to the
question given a passage, involves high-level reasoning processes of
understanding and tracking the relevant contents across various semantic units
such as words, phrases, and sentences in a document. This paper proposes the
novel question-aware sentence gating networks that directly incorporate the
sentence-level information into word-level encoding processes. To this end, our
model first learns question-aware sentence representations and then dynamically
combines them with word-level representations, resulting in semantically
meaningful word representations for QA tasks. Experimental results demonstrate
that our approach consistently improves the accuracy over existing baseline
approaches on various QA datasets and bears the wide applicability to other
neural network-based QA models.
Deep Transfer Learning for Cross-domain Activity Recognition
Authors: Jindong Wang, Vincent W. Zheng, Yiqiang Chen, Meiyu Huang
Date: 2018-07-20 00:00:00
Tasks: Activity Recognition, Cross-Domain Activity Recognition, Human Activity Recognition, Transfer Learning

Human activity recognition plays an important role in people's daily life.
However, it is often expensive and time-consuming to acquire sufficient labeled
activity data. To solve this problem, transfer learning leverages the labeled
samples from the source domain to annotate the target domain which has few or
none labels. Unfortunately, when there are several source domains available, it
is difficult to select the right source domains for transfer. The right source
domain means that it has the most similar properties with the target domain,
thus their similarity is higher, which can facilitate transfer learning.
Choosing the right source domain helps the algorithm perform well and prevents
the negative transfer. In this paper, we propose an effective Unsupervised
Source Selection algorithm for Activity Recognition (USSAR). USSAR is able to
select the most similar $K$ source domains from a list of available domains.
After this, we propose an effective Transfer Neural Network to perform
knowledge transfer for Activity Recognition (TNNAR). TNNAR could capture both
the time and spatial relationship between activities while transferring
knowledge. Experiments on three public activity recognition datasets
demonstrate that: 1) The USSAR algorithm is effective in selecting the best
source domains. 2) The TNNAR method can reach high accuracy when performing
activity knowledge transfer.
A Novel Color Image Enhancement Method by the Transformation of Color Images to 2-D Grayscale Images
Authors: Artyom M. Grigoryan, Aparna John, Sos S. Agaian
Date: 2018-07-20 00:00:00
Tasks: Image Enhancement

A novel method of color image enhancement is proposed, in which three or four
color channels of the image are transformed to one channel 2-D grayscale image.
This paper describes different models of such transformations in the RGB and
other color models. Color image enhancement is achieved by enhancing first the
transformed grayscale image and, then, transforming back the grayscale image
into the colors. The color image enhancement is done on the transformed 2-D
grayscale image rather than on the color image. New algorithms of color image
enhancement are described in both frequency and time domains. The enhancement
by this novel method shows good results. The enhancement of the image is
measured with respect to the metric referred to as the Color Enhancement
Measure Estimation (CEME).
Twitter Sentiment Analysis via Bi-sense Emoji Embedding and Attention-based LSTM
Authors: Yuxiao Chen, Jianbo Yuan, Quanzeng You, Jiebo Luo
Date: 2018-07-20 00:00:00
Tasks: Sentiment Analysis, Twitter Sentiment Analysis

Sentiment analysis on large-scale social media data is important to bridge
the gaps between social media contents and real world activities including
political election prediction, individual and public emotional status
monitoring and analysis, and so on. Although textual sentiment analysis has
been well studied based on platforms such as Twitter and Instagram, analysis of
the role of extensive emoji uses in sentiment analysis remains light. In this
paper, we propose a novel scheme for Twitter sentiment analysis with extra
attention on emojis. We first learn bi-sense emoji embeddings under positive
and negative sentimental tweets individually, and then train a sentiment
classifier by attending on these bi-sense emoji embeddings with an
attention-based long short-term memory network (LSTM). Our experiments show
that the bi-sense embedding is effective for extracting sentiment-aware
embeddings of emojis and outperforms the state-of-the-art models. We also
visualize the attentions to show that the bi-sense emoji embedding provides
better guidance on the attention mechanism to obtain a more robust
understanding of the semantics and sentiments.
Alpha-rooting color image enhancement method by two-side 2-D quaternion discrete Fourier transform followed by spatial transformation
Authors: Artyom M. Grigoryan, Aparna John, Sos S. Agaian
Date: 2018-07-20 00:00:00
Tasks: Image Enhancement

In this paper a quaternion approach of enhancement method is proposed in
which color in the image is considered as a single entity. This new method is
referred as the alpha-rooting method of color image enhancement by the
two-dimensional quaternion discrete Fourier transform (2-D QDFT) followed by a
spatial transformation. The results of the proposed color image enhancement
method are compared with its counterpart channel-by-channel enhancement
algorithm by the 2-D DFT. The image enhancements are quantified to the
enhancement measure that is based on visual perception referred as the color
enhancement measure estimation (CEME). The preliminary experiment results show
that the quaternion approach of image enhancement is an effective color image
enhancement technique.
A Fully Convolutional Neural Network Approach to End-to-End Speech Enhancement
Authors: Frank Longueira, Sam Keene
Date: 2018-07-20 00:00:00
Tasks: Speech Enhancement

This paper will describe a novel approach to the cocktail party problem that
relies on a fully convolutional neural network (FCN) architecture. The FCN
takes noisy audio data as input and performs nonlinear, filtering operations to
produce clean audio data of the target speech at the output. Our method learns
a model for one specific speaker, and is then able to extract that speakers
voice from babble background noise. Results from experimentation indicate the
ability to generalize to new speakers and robustness to new noise environments
of varying signal-to-noise ratios. A potential application of this method would
be for use in hearing aids. A pre-trained model could be quickly fine tuned for
an individuals family members and close friends, and deployed onto a hearing
aid to assist listeners in noisy environments.
Hardware Optimizations of Dense Binary Hyperdimensional Computing: Rematerialization of Hypervectors, Binarized Bundling, and Combinational Associative Memory
Authors: Manuel Schmuck, Luca Benini, Abbas Rahimi
Date: 2018-07-20 00:00:00

Brain-inspired hyperdimensional (HD) computing models neural activity
patterns of the very size of the brain's circuits with points of a
hyperdimensional space, that is, with hypervectors. Hypervectors are
$D$-dimensional (pseudo)random vectors with independent and identically
distributed (i.i.d.) components constituting ultra-wide holographic words: $D =
10,000$ bits, for instance. At its very core, HD computing manipulates a set of
seed hypervectors to build composite hypervectors representing objects of
interest. It demands memory optimizations with simple operations for an e cient
hardware realization. In this paper, we propose hardware techniques for
optimizations of HD computing, in a synthesizable VHDL library, to enable
co-located implementation of both learning and classification tasks on only a
small portion of Xilinx(R) UltraScale(TM) FPGAs: (1) We propose simple logical
operations to rematerialize the hypervectors on the fly rather than loading
them from memory. These operations massively reduce the memory footprint by
directly computing the composite hypervectors whose individual seed
hypervectors do not need to be stored in memory. (2) Bundling a series of
hypervectors over time requires a multibit counter per every hypervector
component. We instead propose a binarized back-to-back bundling without
requiring any counters. This truly enables on-chip learning with minimal
resources as every hypervector component remains binary over the course of
training to avoid otherwise multibit component. (3) For every classification
event, an associative memory is in charge of finding the closest match between
a set of learned hypervectors and a query hypervector by using a distance
metric. This operator is proportional to [...]
Local Popularity and Time in top-N Recommendation
Authors: Vito Walter Anelli, Tommaso Di Noia, Eugenio Di Sciascio, Azzurra Ragone, Joseph Trotta
Date: 2018-07-11 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

Items popularity is a strong signal in recommendation algorithms. It strongly affects collaborative filtering approaches and it has been proven to be a very good baseline in terms of results accuracy. Even though we miss an actual personalization, global popularity can be effectively used to recommend items to users. In this paper we introduce the idea of a time-aware personalized popularity in recommender systems by considering both items popularity among neighbors and how it changes over time. An experimental evaluation shows a highly competitive behavior of the proposed approach, compared to state of the art model-based collaborative approaches, in terms of results accuracy.
Learning to Generate and Reconstruct 3D Meshes with only 2D Supervision
Authors: Paul Henderson, Vittorio Ferrari
Date: 2018-07-24 00:00:00
Tasks: 3D Reconstruction

We present a unified framework tackling two problems: class-specific 3D
reconstruction from a single image, and generation of new 3D shape samples.
These tasks have received considerable attention recently; however, existing
approaches rely on 3D supervision, annotation of 2D images with keypoints or
poses, and/or training with multiple views of each object instance. Our
framework is very general: it can be trained in similar settings to these
existing approaches, while also supporting weaker supervision scenarios.
Importantly, it can be trained purely from 2D images, without ground-truth pose
annotations, and with a single view per instance. We employ meshes as an output
representation, instead of voxels used in most prior work. This allows us to
exploit shading information during training, which previous 2D-supervised
methods cannot. Thus, our method can learn to generate and reconstruct concave
object classes. We evaluate our approach on synthetic data in various settings,
showing that (i) it learns to disentangle shape from pose; (ii) using shading
in the loss improves performance; (iii) our model is comparable or superior to
state-of-the-art voxel-based approaches on quantitative metrics, while
producing results that are visually more pleasing; (iv) it still performs well
when given supervision weaker than in prior works.
GANimation: Anatomically-aware Facial Animation from a Single Image
Authors: Albert Pumarola, Antonio Agudo, Aleix M. Martinez, Alberto Sanfeliu, Francesc Moreno-Noguer
Date: 2018-07-24 00:00:00
Proceeding: ECCV 2018 9
Tasks: Conditional Image Generation, Face Generation, Image-to-Image Translation

Recent advances in Generative Adversarial Networks (GANs) have shown
impressive results for task of facial expression synthesis. The most successful
architecture is StarGAN, that conditions GANs generation process with images of
a specific domain, namely a set of images of persons sharing the same
expression. While effective, this approach can only generate a discrete number
of expressions, determined by the content of the dataset. To address this
limitation, in this paper, we introduce a novel GAN conditioning scheme based
on Action Units (AU) annotations, which describes in a continuous manifold the
anatomical facial movements defining a human expression. Our approach allows
controlling the magnitude of activation of each AU and combine several of them.
Additionally, we propose a fully unsupervised strategy to train the model, that
only requires images annotated with their activated AUs, and exploit attention
mechanisms that make our network robust to changing backgrounds and lighting
conditions. Extensive evaluation show that our approach goes beyond competing
conditional generators both in the capability to synthesize a much wider range
of expressions ruled by anatomically feasible muscle movements, as in the
capacity of dealing with images in the wild.
Visual Dynamics: Stochastic Future Generation via Layered Cross Convolutional Networks
Authors: Tianfan Xue, Jiajun Wu, Katherine L. Bouman, William T. Freeman
Date: 2018-07-24 00:00:00

We study the problem of synthesizing a number of likely future frames from a single input image. In contrast to traditional methods that have tackled this problem in a deterministic or non-parametric way, we propose to model future frames in a probabilistic manner. Our probabilistic model makes it possible for us to sample and synthesize many possible future frames from a single input image. To synthesize realistic movement of objects, we propose a novel network structure, namely a Cross Convolutional Network; this network encodes image and motion information as feature maps and convolutional kernels, respectively. In experiments, our model performs well on synthetic data, such as 2D shapes and animated game sprites, and on real-world video frames. We present analyses of the learned network representations, showing it is implicitly learning a compact encoding of object appearance and motion. We also demonstrate a few of its applications, including visual analogy-making and video extrapolation.
Unsupervised Learning of Latent Physical Properties Using Perception-Prediction Networks
Authors: David Zheng, Vinson Luo, Jiajun Wu, Joshua B. Tenenbaum
Date: 2018-07-24 00:00:00
Tasks: Object, Prediction

We propose a framework for the completely unsupervised learning of latent
object properties from their interactions: the perception-prediction network
(PPN). Consisting of a perception module that extracts representations of
latent object properties and a prediction module that uses those extracted
properties to simulate system dynamics, the PPN can be trained in an end-to-end
fashion purely from samples of object dynamics. The representations of latent
object properties learned by PPNs not only are sufficient to accurately
simulate the dynamics of systems comprised of previously unseen objects, but
also can be translated directly into human-interpretable properties (e.g.,
mass, coefficient of restitution) in an entirely unsupervised manner.
Crucially, PPNs also generalize to novel scenarios: their gradient-based
training can be applied to many dynamical systems and their graph-based
structure functions over systems comprised of different numbers of objects. Our
results demonstrate the efficacy of graph-based neural architectures in
object-centric inference and prediction tasks, and our model has the potential
to discover relevant object properties in systems that are not yet well
understood.
Improving pairwise comparison models using Empirical Bayes shrinkage
Authors: Stephen Ragain, Alexander Peysakhovich, Johan Ugander
Date: 2018-07-24 00:00:00

Comparison data arises in many important contexts, e.g. shopping, web clicks,
or sports competitions. Typically we are given a dataset of comparisons and
wish to train a model to make predictions about the outcome of unseen
comparisons. In many cases available datasets have relatively few comparisons
(e.g. there are only so many NFL games per year) or efficiency is important
(e.g. we want to quickly estimate the relative appeal of a product). In such
settings it is well known that shrinkage estimators outperform maximum
likelihood estimators. A complicating matter is that standard comparison models
such as the conditional multinomial logit model are only models of conditional
outcomes (who wins) and not of comparisons themselves (who competes). As such,
different models of the comparison process lead to different shrinkage
estimators. In this work we derive a collection of methods for estimating the
pairwise uncertainty of pairwise predictions based on different assumptions
about the comparison process. These uncertainty estimates allow us both to
examine model uncertainty as well as perform Empirical Bayes shrinkage
estimation of the model parameters. We demonstrate that our shrunk estimators
outperform standard maximum likelihood methods on real comparison data from
online comparison surveys as well as from several sports contexts.
Deep Learning on Retina Images as Screening Tool for Diagnostic Decision Support
Authors: Maria Camila Alvarez Trivino, Jeremie Despraz, Jesus Alfonso Lopez Sotelo, Carlos Andres Pena
Date: 2018-07-24 00:00:00
Tasks: Diagnostic

In this project, we developed a deep learning system applied to human retina
images for medical diagnostic decision support. The retina images were provided
by EyePACS. These images were used in the framework of a Kaggle contest, whose
purpose to identify diabetic retinopathy signs through an automatic detection
system. Using as inspiration one of the solutions proposed in the contest, we
implemented a model that successfully detects diabetic retinopathy from retina
images. After a carefully designed preprocessing, the images were used as input
to a deep convolutional neural network (CNN). The CNN performed a feature
extraction process followed by a classification stage, which allowed the system
to differentiate between healthy and ill patients using five categories. Our
model was able to identify diabetic retinopathy in the patients with an
agreement rate of 76.73% with respect to the medical expert's labels for the
test data.
Multi-Class Lesion Diagnosis with Pixel-wise Classification Network
Authors: Manu Goyal, Jiahua Ng, Moi Hoon Yap
Date: 2018-07-24 00:00:00
Tasks: Classification, General Classification

Lesion diagnosis of skin lesions is a very challenging task due to high
inter-class similarities and intra-class variations in terms of color, size,
site and appearance among different skin lesions. With the emergence of
computer vision especially deep learning algorithms, lesion diagnosis is made
possible using these algorithms trained on dermoscopic images. Usually, deep
classification networks are used for the lesion diagnosis to determine
different types of skin lesions. In this work, we used pixel-wise
classification network to provide lesion diagnosis rather than classification
network. We propose to use DeeplabV3+ for multi-class lesion diagnosis in
dermoscopic images of Task 3 of ISIC Challenge 2018. We used various
post-processing methods with DeeplabV3+ to determine the lesion diagnosis in
this challenge and submitted the test results.
Deterministic Fitting of Multiple Structures using Iterative MaxFS with Inlier Scale Estimation and Subset Updating
Authors: Kwang Hee Lee, Sang Wook Lee
Date: 2018-07-24 00:00:00
Tasks: global-optimization

We present an efficient deterministic hypothesis generation algorithm for
robust fitting of multiple structures based on the maximum feasible subsystem
(MaxFS) framework. Despite its advantage, a global optimization method such as
MaxFS has two main limitations for geometric model fitting. First, its
performance is much influenced by the user-specified inlier scale. Second, it
is computationally inefficient for large data. The presented MaxFS-based
algorithm iteratively estimates model parameters and inlier scale and also
overcomes the second limitation by reducing data for the MaxFS problem. Further
it generates hypotheses only with top-n ranked subsets based on matching scores
and data fitting residuals. This reduction of data for the MaxFS problem makes
the algorithm computationally realistic. Our method, called iterative MaxFS
with inlier scale estimation and subset updating (IMaxFS-ISE-SU) in this paper,
performs hypothesis generation and fitting alternately until all of true
structures are found. The IMaxFS-ISE-SU algorithm generates substantially more
reliable hypotheses than random sampling-based methods especially as
(pseudo-)outlier ratios increase. Experimental results demonstrate that our
method can generate more reliable and consistent hypotheses than random
sampling-based methods for estimating multiple structures from data with many
outliers.
Face Mask Extraction in Video Sequence
Authors: Yujiang Wang, Bingnan Luo, Jie Shen, Maja Pantic
Date: 2018-07-24 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Inspired by the recent development of deep network-based methods in semantic image segmentation, we introduce an end-to-end trainable model for face mask extraction in video sequence. Comparing to landmark-based sparse face shape representation, our method can produce the segmentation masks of individual facial components, which can better reflect their detailed shape variations. By integrating Convolutional LSTM (ConvLSTM) algorithm with Fully Convolutional Networks (FCN), our new ConvLSTM-FCN model works on a per-sequence basis and takes advantage of the temporal correlation in video clips. In addition, we also propose a novel loss function, called Segmentation Loss, to directly optimise the Intersection over Union (IoU) performances. In practice, to further increase segmentation accuracy, one primary model and two additional models were trained to focus on the face, eyes, and mouth regions, respectively. Our experiment shows the proposed method has achieved a 16.99% relative improvement (from 54.50% to 63.76% mean IoU) over the baseline FCN model on the 300 Videos in the Wild (300VW) dataset.
Theoretical Perspective of Convergence Complexity of Evolutionary Algorithms Adopting Optimal Mixing
Authors: Yu-Fan Tung, Tian-Li Yu
Date: 2018-07-24 00:00:00
Tasks: Evolutionary Algorithms

The optimal mixing evolutionary algorithms (OMEAs) have recently drawn much
attention for their robustness, small size of required population, and
efficiency in terms of number of function evaluations (NFE). In this paper, the
performances and behaviors of OMEAs are studied by investigating the mechanism
of optimal mixing (OM), the variation operator in OMEAs, under two scenarios --
one-layer and two-layer masks. For the case of one-layer masks, the required
population size is derived from the viewpoint of initial supply, while the
convergence time is derived by analyzing the progress of sub-solution growth.
NFE is then asymptotically bounded with rational probability by estimating the
probability of performing evaluations. For the case of two-layer masks,
empirical results indicate that the required population size is proportional to
both the degree of cross competition and the results from the one-layer-mask
case. The derived models also indicate that population sizing is decided by
initial supply when disjoint masks are adopted, that the high selection
pressure imposed by OM makes the composition of sub-problems impact little on
NFE, and that the population size requirement for two-layer masks increases
with the reverse-growth probability.