pwc-archive/papers-with-abstracts huggingface dataset:
Semantic Sentiment Analysis of Twitter Data
Authors: Preslav Nakov
Date: 2017-10-04 00:00:00
Tasks: Marketing, Sentiment Analysis

Internet and the proliferation of smart mobile devices have changed the way
information is created, shared, and spreads, e.g., microblogs such as Twitter,
weblogs such as LiveJournal, social networks such as Facebook, and instant
messengers such as Skype and WhatsApp are now commonly used to share thoughts
and opinions about anything in the surrounding world. This has resulted in the
proliferation of social media content, thus creating new opportunities to study
public opinion at a scale that was never possible before. Naturally, this
abundance of data has quickly attracted business and research interest from
various fields including marketing, political science, and social studies,
among many others, which are interested in questions like these: Do people like
the new Apple Watch? Do Americans support ObamaCare? How do Scottish feel about
the Brexit? Answering these questions requires studying the sentiment of
opinions people express in social media, which has given rise to the fast
growth of the field of sentiment analysis in social media, with Twitter being
especially popular for research due to its scale, representativeness, variety
of topics discussed, as well as ease of public access to its messages. Here we
present an overview of work on sentiment analysis on Twitter.
Multifractal analysis of the time series of daily means of wind speed in complex regions
Authors: Mohamed Laib, Jean Golay, Luciano Telesca, Mikhail Kanevski
Date: 2017-10-04 00:00:00
Tasks: Time Series, Time Series Analysis

In this paper, we applied the multifractal detrended fluctuation analysis to
the daily means of wind speed measured by 119 weather stations distributed over
the territory of Switzerland. The analysis was focused on the inner time
fluctuations of wind speed, which could be more linked with the local
conditions of the highly varying topography of Switzerland. Our findings point
out to a persistent behaviour of all the measured wind speed series (indicated
by a Hurst exponent significantly larger than 0.5), and to a high
multifractality degree indicating a relative dominance of the large
fluctuations in the dynamics of wind speed, especially in the Swiss plateau,
which is comprised between the Jura and Alp mountain ranges. The study
represents a contribution to the understanding of the dynamical mechanisms of
wind speed variability in mountainous regions.
MMCR4NLP: Multilingual Multiway Corpora Repository for Natural Language Processing
Authors: Raj Dabre, Sadao Kurohashi
Date: 2017-10-03 00:00:00
Tasks: Machine Translation, Multilingual NLP, Sentence, Transfer Learning, Translation

Multilinguality is gradually becoming ubiquitous in the sense that more and
more researchers have successfully shown that using additional languages help
improve the results in many Natural Language Processing tasks. Multilingual
Multiway Corpora (MMC) contain the same sentence in multiple languages. Such
corpora have been primarily used for Multi-Source and Pivot Language Machine
Translation but are also useful for developing multilingual sequence taggers by
transfer learning. While these corpora are available, they are not organized
for multilingual experiments and researchers need to write boilerplate code
every time they want to use said corpora. Moreover, because there is no
official MMC collection it becomes difficult to compare against existing
approaches. As such we present our work on creating a unified and
systematically organized repository of MMC spanning a large number of
languages. We also provide training, development and test splits for corpora
where official splits are unavailable. We hope that this will help speed up the
pace of multilingual NLP research and ensure that NLP researchers obtain
results that are more trustable since they can be compared easily. We indicate
corpora sources, extraction procedures if any and relevant statistics. We also
make our collection public for research purposes.
Cross-Language Question Re-Ranking
Authors: Giovanni Da San Martino, Salvatore Romeo, Alberto Barron-Cedeno, Shafiq Joty, Lluis Marquez, Alessandro Moschitti, Preslav Nakov
Date: 2017-10-04 00:00:00
Tasks: Machine Translation, Re-Ranking, Word Embeddings

We study how to find relevant questions in community forums when the language
of the new questions is different from that of the existing questions in the
forum. In particular, we explore the Arabic-English language pair. We compare a
kernel-based system with a feed-forward neural network in a scenario where a
large parallel corpus is available for training a machine translation system,
bilingual dictionaries, and cross-language word embeddings. We observe that
both approaches degrade the performance of the system when working on the
translated text, especially the kernel-based system, which depends heavily on a
syntactic kernel. We address this issue using a cross-language tree kernel,
which compares the original Arabic tree to the English trees of the related
questions. We show that this kernel almost closes the performance gap with
respect to the monolingual system. On the neural network side, we use the
parallel corpus to train cross-language embeddings, which we then use to
represent the Arabic input and the English related questions in the same space.
The results also improve to close to those of the monolingual neural network.
Overall, the kernel system shows a better performance compared to the neural
network in all cases.
Modular Representation of Layered Neural Networks
Authors: Chihiro Watanabe, Kaoru Hiramatsu, Kunio Kashino
Date: 2017-03-01 00:00:00
Tasks: speech-recognition, Speech Recognition

Layered neural networks have greatly improved the performance of various
applications including image processing, speech recognition, natural language
processing, and bioinformatics. However, it is still difficult to discover or
interpret knowledge from the inference provided by a layered neural network,
since its internal representation has many nonlinear and complex parameters
embedded in hierarchical layers. Therefore, it becomes important to establish a
new methodology by which layered neural networks can be understood.
  In this paper, we propose a new method for extracting a global and simplified
structure from a layered neural network. Based on network analysis, the
proposed method detects communities or clusters of units with similar
connection patterns. We show its effectiveness by applying it to three use
cases. (1) Network decomposition: it can decompose a trained neural network
into multiple small independent networks thus dividing the problem and reducing
the computation time. (2) Training assessment: the appropriateness of a trained
result with a given hyperparameter or randomly chosen initial parameters can be
evaluated by using a modularity index. And (3) data analysis: in practical data
it reveals the community structure in the input, hidden, and output layers,
which serves as a clue for discovering knowledge from a trained neural network.
Weakly supervised 3D Reconstruction with Adversarial Constraint
Authors: JunYoung Gwak, Christopher B. Choy, Animesh Garg, Manmohan Chandraker, Silvio Savarese
Date: 2017-05-31 00:00:00
Tasks: 3D Reconstruction

Supervised 3D reconstruction has witnessed a significant progress through the
use of deep neural networks. However, this increase in performance requires
large scale annotations of 2D/3D data. In this paper, we explore inexpensive 2D
supervision as an alternative for expensive 3D CAD annotation. Specifically, we
use foreground masks as weak supervision through a raytrace pooling layer that
enables perspective projection and backpropagation. Additionally, since the 3D
reconstruction from masks is an ill posed problem, we propose to constrain the
3D reconstruction to the manifold of unlabeled realistic 3D shapes that match
mask observations. We demonstrate that learning a log-barrier solution to this
constrained optimization problem resembles the GAN objective, enabling the use
of existing tools for training GANs. We evaluate and analyze the manifold
constrained reconstruction on various datasets for single and multi-view
reconstruction of both synthetic and real images.
Secrets in Computing Optical Flow by Convolutional Networks
Authors: Junxuan Li
Date: 2017-10-04 00:00:00
Tasks: General Classification, Optical Flow Estimation, regression

Convolutional neural networks (CNNs) have been widely used over many areas in
compute vision. Especially in classification. Recently, FlowNet and several
works on opti- cal estimation using CNNs shows the potential ability of CNNs in
doing per-pixel regression. We proposed several CNNs network architectures that
can estimate optical flow, and fully unveiled the intrinsic different between
these structures.
Content-Adaptive Sketch Portrait Generation by Decompositional Representation Learning
Authors: Dongyu Zhang, Liang Lin, Tianshui Chen, Xian Wu, Wenwei Tan, Ebroul Izquierdo
Date: 2017-10-04 00:00:00
Tasks: Representation Learning

Sketch portrait generation benefits a wide range of applications such as
digital entertainment and law enforcement. Although plenty of efforts have been
dedicated to this task, several issues still remain unsolved for generating
vivid and detail-preserving personal sketch portraits. For example, quite a few
artifacts may exist in synthesizing hairpins and glasses, and textural details
may be lost in the regions of hair or mustache. Moreover, the generalization
ability of current systems is somewhat limited since they usually require
elaborately collecting a dictionary of examples or carefully tuning
features/components. In this paper, we present a novel representation learning
framework that generates an end-to-end photo-sketch mapping through structure
and texture decomposition. In the training stage, we first decompose the input
face photo into different components according to their representational
contents (i.e., structural and textural parts) by using a pre-trained
Convolutional Neural Network (CNN). Then, we utilize a Branched Fully
Convolutional Neural Network (BFCN) for learning structural and textural
representations, respectively. In addition, we design a Sorted Matching Mean
Square Error (SM-MSE) metric to measure texture patterns in the loss function.
In the stage of sketch rendering, our approach automatically generates
structural and textural representations for the input photo and produces the
final result via a probabilistic fusion scheme. Extensive experiments on
several challenging benchmarks suggest that our approach outperforms
example-based synthesis algorithms in terms of both perceptual and objective
metrics. In addition, the proposed method also has better generalization
ability across dataset without additional training.
Feasibility Study: Moving Non-Homogeneous Teams in Congested Video Game Environments
Authors: Hang Ma, Jingxing Yang, Liron Cohen, T. K. Satish Kumar, Sven Koenig
Date: 2017-10-04 00:00:00
Tasks: Multi-Agent Path Finding

Multi-agent path finding (MAPF) is a well-studied problem in artificial
intelligence, where one needs to find collision-free paths for agents with
given start and goal locations. In video games, agents of different types often
form teams. In this paper, we demonstrate the usefulness of MAPF algorithms
from artificial intelligence for moving such non-homogeneous teams in congested
video game environments.
Incorporating Depth into both CNN and CRF for Indoor Semantic Segmentation
Authors: Jindong Jiang, Zhijun Zhang, Yongqian Huang, Lunan Zheng
Date: 2017-05-21 00:00:00
Tasks: Semantic Segmentation

To improve segmentation performance, a novel neural network architecture
(termed DFCN-DCRF) is proposed, which combines an RGB-D fully convolutional
neural network (DFCN) with a depth-sensitive fully-connected conditional random
field (DCRF). First, a DFCN architecture which fuses depth information into the
early layers and applies dilated convolution for later contextual reasoning is
designed. Then, a depth-sensitive fully-connected conditional random field
(DCRF) is proposed and combined with the previous DFCN to refine the
preliminary result. Comparative experiments show that the proposed DFCN-DCRF
has the best performance compared with most state-of-the-art methods.
A Survey of Multithreading Image Analysis
Authors: Elham Sagheb Hossein Pour
Date: 2015-06-15 00:00:00
Tasks: Survey

Digital image analysis has made a big advance in many areas of enterprise
applications including medicine, industry, and entertainment by assisting the
extraction of semantic information from digital images. However, its large
computational complexity has been a trouble to most real-time developments.
While image analysis in general has been studied for a log period in computer
science community, the use of multithreading strategy as the most efficient
improving computational capacity technique has been limited so far. In this
survey an attempt is made to explain the current knowledge and so far
progresses in incorporating image analysis with multithreading approaches. The
present work also provides insights and tendencies for the possible future
enhancement of multithreading image analysis.
Duality of Graphical Models and Tensor Networks
Authors: Elina Robeva, Anna Seigal
Date: 2017-10-04 00:00:00
Tasks: Tensor Networks

In this article we show the duality between tensor networks and undirected
graphical models with discrete variables. We study tensor networks on
hypergraphs, which we call tensor hypernetworks. We show that the tensor
hypernetwork on a hypergraph exactly corresponds to the graphical model given
by the dual hypergraph. We translate various notions under duality. For
example, marginalization in a graphical model is dual to contraction in the
tensor network. Algorithms also translate under duality. We show that belief
propagation corresponds to a known algorithm for tensor network contraction.
This article is a reminder that the research areas of graphical models and
tensor networks can benefit from interaction.
Effective Image Differencing with ConvNets for Real-time Transient Hunting
Authors: Nima Sedaghat, Ashish Mahabal
Date: 2017-10-04 00:00:00
Tasks: Image Registration

Large sky surveys are increasingly relying on image subtraction pipelines for
real-time (and archival) transient detection. In this process one has to
contend with varying PSF, small brightness variations in many sources, as well
as artifacts resulting from saturated stars, and, in general, matching errors.
Very often the differencing is done with a reference image that is deeper than
individual images and the attendant difference in noise characteristics can
also lead to artifacts. We present here a deep-learning approach to transient
detection that encapsulates all the steps of a traditional image subtraction
pipeline -- image registration, background subtraction, noise removal, psf
matching, and subtraction -- into a single real-time convolutional network.
Once trained the method works lighteningly fast, and given that it does
multiple steps at one go, the advantages for multi-CCD, fast surveys like ZTF
and LSST are obvious.
Usable & Scalable Learning Over Relational Data With Automatic Language Bias
Authors: Jose Picado, Arash Termehchy, Sudhanshu Pathak, Alan Fern, Praveen Ilango, Yunqiao Cai
Date: 2017-10-03 00:00:00
Tasks: Relational Reasoning

Relational databases are valuable resources for learning novel and interesting relations and concepts. In order to constraint the search through the large space of candidate definitions, users must tune the algorithm by specifying a language bias. Unfortunately, specifying the language bias is done via trial and error and is guided by the expert's intuitions. We propose AutoBias, a system that leverages information in the schema and content of the database to automatically induce the language bias used by popular relational learning systems. We show that AutoBias delivers the same accuracy as using manually-written language bias by imposing only a slight overhead on the running time of the learning algorithm.
Smoothness-based Edge Detection using Low-SNR Camera for Robot Navigation
Authors: Vu Hoang Minh, Tajwar Abrar Aleef, Usama Pervaiz, Yeman Brhane Hagos, Saed Khawaldeh
Date: 2017-10-03 00:00:00
Tasks: Edge Detection, Robot Navigation

In the emerging advancement in the branch of autonomous robotics, the ability
of a robot to efficiently localize and construct maps of its surrounding is
crucial. This paper deals with utilizing thermal-infrared cameras, as opposed
to conventional cameras as the primary sensor to capture images of the robot's
surroundings. For localization, the images need to be further processed before
feeding them to a navigational system. The main motivation of this paper was to
develop an edge detection methodology capable of utilizing the low-SNR poor
output from such a thermal camera and effectively detect smooth edges of the
surrounding environment. The enhanced edge detector proposed in this paper
takes the raw image from the thermal sensor, denoises the images, applies Canny
edge detection followed by CSS method. The edges are ranked to remove any noise
and only edges of the highest rank are kept. Then, the broken edges are linked
by computing edge metrics and a smooth edge of the surrounding is displayed in
a binary image. Several comparisons are also made in the paper between the
proposed technique and the existing techniques.
Transferring Semantic Roles Using Translation and Syntactic Information
Authors: Maryam Aminian, Mohammad Sadegh Rasooli, Mona Diab
Date: 2017-10-03 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Semantic Role Labeling, Translation, Word Alignment

Our paper addresses the problem of annotation projection for semantic role
labeling for resource-poor languages using supervised annotations from a
resource-rich language through parallel data. We propose a transfer method that
employs information from source and target syntactic dependencies as well as
word alignment density to improve the quality of an iterative bootstrapping
method. Our experiments yield a $3.5$ absolute labeled F-score improvement over
a standard annotation projection method.
A Fully Convolutional Network for Semantic Labeling of 3D Point Clouds
Authors: Mohammed Yousefhussien, David J. Kelbe, Emmett J. Ientilucci, Carl Salvaggio
Date: 2017-10-03 00:00:00
Tasks: 2D Semantic Segmentation, Semantic Segmentation

When classifying point clouds, a large amount of time is devoted to the
process of engineering a reliable set of features which are then passed to a
classifier of choice. Generally, such features - usually derived from the
3D-covariance matrix - are computed using the surrounding neighborhood of
points. While these features capture local information, the process is usually
time-consuming, and requires the application at multiple scales combined with
contextual methods in order to adequately describe the diversity of objects
within a scene. In this paper we present a 1D-fully convolutional network that
consumes terrain-normalized points directly with the corresponding spectral
data,if available, to generate point-wise labeling while implicitly learning
contextual features in an end-to-end fashion. Our method uses only the
3D-coordinates and three corresponding spectral features for each point.
Spectral features may either be extracted from 2D-georeferenced images, as
shown here for Light Detection and Ranging (LiDAR) point clouds, or extracted
directly for passive-derived point clouds,i.e. from muliple-view imagery. We
train our network by splitting the data into square regions, and use a pooling
layer that respects the permutation-invariance of the input points. Evaluated
using the ISPRS 3D Semantic Labeling Contest, our method scored second place
with an overall accuracy of 81.6%. We ranked third place with a mean F1-score
of 63.32%, surpassing the F1-score of the method with highest accuracy by
1.69%. In addition to labeling 3D-point clouds, we also show that our method
can be easily extended to 2D-semantic segmentation tasks, with promising
initial results.
GaDei: On Scale-up Training As A Service For Deep Learning
Authors: Wei Zhang, Minwei Feng, Yunhui Zheng, Yufei Ren, Yandong Wang, Ji Liu, Peng Liu, Bing Xiang, Li Zhang, Bo-Wen Zhou, Fei Wang
Date: 2016-11-18 00:00:00

Deep learning (DL) training-as-a-service (TaaS) is an important emerging
industrial workload. The unique challenge of TaaS is that it must satisfy a
wide range of customers who have no experience and resources to tune DL
hyper-parameters, and meticulous tuning for each user's dataset is
prohibitively expensive. Therefore, TaaS hyper-parameters must be fixed with
values that are applicable to all users. IBM Watson Natural Language Classifier
(NLC) service, the most popular IBM cognitive service used by thousands of
enterprise-level clients around the globe, is a typical TaaS service. By
evaluating the NLC workloads, we show that only the conservative
hyper-parameter setup (e.g., small mini-batch size and small learning rate) can
guarantee acceptable model accuracy for a wide range of customers. We further
justify theoretically why such a setup guarantees better model convergence in
general. Unfortunately, the small mini-batch size causes a high volume of
communication traffic in a parameter-server based system. We characterize the
high communication bandwidth requirement of TaaS using representative
industrial deep learning workloads and demonstrate that none of the
state-of-the-art scale-up or scale-out solutions can satisfy such a
requirement. We then present GaDei, an optimized shared-memory based scale-up
parameter server design. We prove that the designed protocol is deadlock-free
and it processes each gradient exactly once. Our implementation is evaluated on
both commercial benchmarks and public benchmarks to demonstrate that it
significantly outperforms the state-of-the-art parameter-server based
implementation while maintaining the required accuracy and our implementation
reaches near the best possible runtime performance, constrained only by the
hardware limitation. Furthermore, to the best of our knowledge, GaDei is the
only scale-up DL system that provides fault-tolerance.
Understanding the visual speech signal
Authors: Helen L. Bear
Date: 2017-10-03 00:00:00
Tasks: Lipreading

For machines to lipread, or understand speech from lip movement, they decode
lip-motions (known as visemes) into the spoken sounds. We investigate the
visual speech channel to further our understanding of visemes. This has
applications beyond machine lipreading; speech therapists, animators, and
psychologists can benefit from this work. We explain the influence of speaker
individuality, and demonstrate how one can use visemes to boost lipreading.
Simple Cortex: A Model of Cells in the Sensory Nervous System
Authors: David Di Giorgio
Date: 2017-10-03 00:00:00

Neuroscience research has produced many theories and computational neural
models of sensory nervous systems. Notwithstanding many different perspectives
towards developing intelligent machines, artificial intelligence has ultimately
been influenced by neuroscience. Therefore, this paper provides an introduction
to biologically inspired machine intelligence by exploring the basic principles
of sensation and perception as well as the structure and behavior of biological
sensory nervous systems like the neocortex. Concepts like spike timing,
synaptic plasticity, inhibition, neural structure, and neural behavior are
applied to a new model, Simple Cortex (SC). A software implementation of SC has
been built and demonstrates fast observation, learning, and prediction of
spatio-temporal sensory-motor patterns and sequences. Finally, this paper
suggests future areas of improvement and growth for Simple Cortex and other
related machine intelligence models.