pwc-archive/papers-with-abstracts huggingface dataset:
Text Recognition in Scene Image and Video Frame using Color Channel Selection
Authors: Ayan Kumar Bhunia, Gautam Kumar, Partha Pratim Roy, R. Balasubramanian, Umapada Pal
Date: 2017-07-21 00:00:00
Tasks: Binarization, channel selection, Optical Character Recognition (OCR)

In recent years, recognition of text from natural scene image and video frame
has got increased attention among the researchers due to its various
complexities and challenges. Because of low resolution, blurring effect,
complex background, different fonts, color and variant alignment of text within
images and video frames, etc., text recognition in such scenario is difficult.
Most of the current approaches usually apply a binarization algorithm to
convert them into binary images and next OCR is applied to get the recognition
result. In this paper, we present a novel approach based on color channel
selection for text recognition from scene images and video frames. In the
approach, at first, a color channel is automatically selected and then selected
color channel is considered for text recognition. Our text recognition
framework is based on Hidden Markov Model (HMM) which uses Pyramidal Histogram
of Oriented Gradient features extracted from selected color channel. From each
sliding window of a color channel our color-channel selection approach analyzes
the image properties from the sliding window and then a multi-label Support
Vector Machine (SVM) classifier is applied to select the color channel that
will provide the best recognition results in the sliding window. This color
channel selection for each sliding window has been found to be more fruitful
than considering a single color channel for the whole word image. Five
different features have been analyzed for multi-label SVM based color channel
selection where wavelet transform based feature outperforms others. Our
framework has been tested on different publicly available scene/video text
image datasets. For Devanagari script, we collected our own data dataset. The
performances obtained from experimental results are encouraging and show the
advantage of the proposed method.
HMM-based Writer Identification in Music Score Documents without Staff-Line Removal
Authors: Partha Pratim Roy, Ayan Kumar Bhunia, Umapada Pal
Date: 2017-07-21 00:00:00
Tasks: feature selection, Line Detection

Writer identification from musical score documents is a challenging task due
to its inherent problem of overlapping of musical symbols with staff lines.
Most of the existing works in the literature of writer identification in
musical score documents were performed after a preprocessing stage of staff
lines removal. In this paper we propose a novel writer identification framework
in musical documents without removing staff lines from documents. In our
approach, Hidden Markov Model has been used to model the writing style of the
writers without removing staff lines. The sliding window features are extracted
from musical score lines and they are used to build writer specific HMM models.
Given a query musical sheet, writer specific confidence for each musical line
is returned by each writer specific model using a loglikelihood score. Next, a
loglikelihood score in page level is computed by weighted combination of these
scores from the corresponding line images of the page. A novel Factor Analysis
based feature selection technique is applied in sliding window features to
reduce the noise appearing from staff lines which proves efficiency in writer
identification performance.In our framework we have also proposed a novel score
line detection approach in musical sheet using HMM. The experiment has been
performed in CVC-MUSCIMA dataset and the results obtained that the proposed
approach is efficient for score line detection and writer identification
without removing staff lines. To get the idea of computation time of our
method, detail analysis of execution time is also provided.
Tartan: Accelerating Fully-Connected and Convolutional Layers in Deep Learning Networks by Exploiting Numerical Precision Variability
Authors: Alberto Delmas, Sayeh Sharify, Patrick Judd, Andreas Moshovos
Date: 2017-07-27 00:00:00
Tasks: image-classification, Image Classification

Tartan (TRT), a hardware accelerator for inference with Deep Neural Networks
(DNNs), is presented and evaluated on Convolutional Neural Networks. TRT
exploits the variable per layer precision requirements of DNNs to deliver
execution time that is proportional to the precision p in bits used per layer
for convolutional and fully-connected layers. Prior art has demonstrated an
accelerator with the same execution performance only for convolutional layers.
Experiments on image classification CNNs show that on average across all
networks studied, TRT outperforms a state-of-the-art bit-parallel accelerator
by 1:90x without any loss in accuracy while it is 1:17x more energy efficient.
TRT requires no network retraining while it enables trading off accuracy for
additional improvements in execution performance and energy efficiency. For
example, if a 1% relative loss in accuracy is acceptable, TRT is on average
2:04x faster and 1:25x more energy efficient than a conventional bit-parallel
accelerator. A Tartan configuration that processes 2-bits at time, requires
less area than the 1-bit configuration, improves efficiency to 1:24x over the
bit-parallel baseline while being 73% faster for convolutional layers and 60%
faster for fully-connected layers is also presented.
Adapting Sequence Models for Sentence Correction
Authors: Allen Schmaltz, Yoon Kim, Alexander M. Rush, Stuart M. Shieber
Date: 2017-07-27 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Machine Translation, Sentence, Translation

In a controlled experiment of sequence-to-sequence approaches for the task of
sentence correction, we find that character-based models are generally more
effective than word-based models and models that encode subword information via
convolutions, and that modeling the output data as a series of diffs improves
effectiveness over standard approaches. Our strongest sequence-to-sequence
model improves over our strongest phrase-based statistical machine translation
model, with access to the same data, by 6 M2 (0.5 GLEU) points. Additionally,
in the data environment of the standard CoNLL-2014 setup, we demonstrate that
modeling (and tuning against) diffs yields similar or better M2 scores with
simpler models and/or significantly less data than previous
sequence-to-sequence approaches.
Improving the Accuracy of Stereo Visual Odometry Using Visual Illumination Estimation
Authors: Lee Clement, Valentin Peretroukhin, Jonathan Kelly
Date: 2016-09-15 00:00:00
Tasks: Visual Odometry

In the absence of reliable and accurate GPS, visual odometry (VO) has emerged as an effective means of estimating the egomotion of robotic vehicles. Like any dead-reckoning technique, VO suffers from unbounded accumulation of drift error over time, but this accumulation can be limited by incorporating absolute orientation information from, for example, a sun sensor. In this paper, we leverage recent work on visual outdoor illumination estimation to show that estimation error in a stereo VO pipeline can be reduced by inferring the sun position from the same image stream used to compute VO, thereby gaining the benefits of sun sensing without requiring a dedicated sun sensor or the sun to be visible to the camera. We compare sun estimation methods based on hand-crafted visual cues and Convolutional Neural Networks (CNNs) and demonstrate our approach on a combined 7.8 km of urban driving from the popular KITTI dataset, achieving up to a 43% reduction in translational average root mean squared error (ARMSE) and a 59% reduction in final translational drift error compared to pure VO alone.
Bandit Convex Optimization for Scalable and Dynamic IoT Management
Authors: Tianyi Chen, Georgios B. Giannakis
Date: 2017-07-27 00:00:00
Tasks: Management

The present paper deals with online convex optimization involving both
time-varying loss functions, and time-varying constraints. The loss functions
are not fully accessible to the learner, and instead only the function values
(a.k.a. bandit feedback) are revealed at queried points. The constraints are
revealed after making decisions, and can be instantaneously violated, yet they
must be satisfied in the long term. This setting fits nicely the emerging
online network tasks such as fog computing in the Internet-of-Things (IoT),
where online decisions must flexibly adapt to the changing user preferences
(loss functions), and the temporally unpredictable availability of resources
(constraints). Tailored for such human-in-the-loop systems where the loss
functions are hard to model, a family of bandit online saddle-point (BanSaP)
schemes are developed, which adaptively adjust the online operations based on
(possibly multiple) bandit feedback of the loss functions, and the changing
environment. Performance here is assessed by: i) dynamic regret that
generalizes the widely used static regret; and, ii) fit that captures the
accumulated amount of constraint violations. Specifically, BanSaP is proved to
simultaneously yield sub-linear dynamic regret and fit, provided that the best
dynamic solutions vary slowly over time. Numerical tests in fog computation
offloading tasks corroborate that our proposed BanSaP approach offers
competitive performance relative to existing approaches that are based on
gradient feedback.
A Shared Task on Bandit Learning for Machine Translation
Authors: Artem Sokolov, Julia Kreutzer, Kellen Sunderland, Pavel Danchenko, Witold Szymaniak, Hagen Fürstenau, Stefan Riezler
Date: 2017-07-27 00:00:00
Proceeding: WS 2017 9
Tasks: Machine Translation, Translation

We introduce and describe the results of a novel shared task on bandit
learning for machine translation. The task was organized jointly by Amazon and
Heidelberg University for the first time at the Second Conference on Machine
Translation (WMT 2017). The goal of the task is to encourage research on
learning machine translation from weak user feedback instead of human
references or post-edits. On each of a sequence of rounds, a machine
translation system is required to propose a translation for an input, and
receives a real-valued estimate of the quality of the proposed translation for
learning. This paper describes the shared task's learning and evaluation setup,
using services hosted on Amazon Web Services (AWS), the data and evaluation
metrics, and the results of various machine translation architectures and
learning protocols.
A Locally Adapting Technique for Boundary Detection using Image Segmentation
Authors: Marylesa Howard, Margaret C. Hock, B. T. Meehan, Leora Dresselhaus-Cooper
Date: 2017-07-27 00:00:00
Tasks: Boundary Detection, Image Segmentation, Segmentation, Semantic Segmentation

Rapid growth in the field of quantitative digital image analysis is paving
the way for researchers to make precise measurements about objects in an image.
To compute quantities from the image such as the density of compressed
materials or the velocity of a shockwave, we must determine object boundaries.
Images containing regions that each have a spatial trend in intensity are of
particular interest. We present a supervised image segmentation method that
incorporates spatial information to locate boundaries between regions with
overlapping intensity histograms. The segmentation of a pixel is determined by
comparing its intensity to distributions from local, nearby pixel intensities.
Because of the statistical nature of the algorithm, we use maximum likelihood
estimation theory to quantify uncertainty about each boundary. We demonstrate
the success of this algorithm on a radiograph of a multicomponent cylinder and
on an optical image of a laser-induced shockwave, and we provide final boundary
locations with associated bands of uncertainty.
Neural Offset Min-Sum Decoding
Authors: Loren Lugosch, Warren J. Gross
Date: 2017-01-20 00:00:00
Tasks: Decoder

Recently, it was shown that if multiplicative weights are assigned to the
edges of a Tanner graph used in belief propagation decoding, it is possible to
use deep learning techniques to find values for the weights which improve the
error-correction performance of the decoder. Unfortunately, this approach
requires many multiplications, which are generally expensive operations. In
this paper, we suggest a more hardware-friendly approach in which offset
min-sum decoding is augmented with learnable offset parameters. Our method uses
no multiplications and has a parameter count less than half that of the
multiplicative algorithm. This both speeds up training and provides a feasible
path to hardware architectures. After describing our method, we compare the
performance of the two neural decoding algorithms and show that our method
achieves error-correction performance within 0.1 dB of the multiplicative
approach and as much as 1 dB better than traditional belief propagation for the
codes under consideration.
Image Based Appraisal of Real Estate Properties
Authors: Quanzeng You, Ran Pang, Liangliang Cao, Jiebo Luo
Date: 2016-11-28 00:00:00

Real estate appraisal, which is the process of estimating the price for real
estate properties, is crucial for both buys and sellers as the basis for
negotiation and transaction. Traditionally, the repeat sales model has been
widely adopted to estimate real estate price. However, it depends the design
and calculation of a complex economic related index, which is challenging to
estimate accurately. Today, real estate brokers provide easy access to detailed
online information on real estate properties to their clients. We are
interested in estimating the real estate price from these large amounts of
easily accessed data. In particular, we analyze the prediction power of online
house pictures, which is one of the key factors for online users to make a
potential visiting decision. The development of robust computer vision
algorithms makes the analysis of visual content possible. In this work, we
employ a Recurrent Neural Network (RNN) to predict real estate price using the
state-of-the-art visual features. The experimental results indicate that our
model outperforms several of other state-of-the-art baseline algorithms in
terms of both mean absolute error (MAE) and mean absolute percentage error
(MAPE).
Effective Inference for Generative Neural Parsing
Authors: Mitchell Stern, Daniel Fried, Dan Klein
Date: 2017-07-27 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Constituency Parsing, Reranking

Generative neural models have recently achieved state-of-the-art results for
constituency parsing. However, without a feasible search procedure, their use
has so far been limited to reranking the output of external parsers in which
decoding is more tractable. We describe an alternative to the conventional
action-level beam search used for discriminative neural models that enables us
to decode directly in these generative models. We then show that by improving
our basic candidate selection strategy and using a coarse pruning function, we
can improve accuracy while exploring significantly less of the search space.
Applied to the model of Choe and Charniak (2016), our inference procedure
obtains 92.56 F1 on section 23 of the Penn Treebank, surpassing prior
state-of-the-art results for single-model systems.
Building Detection from Satellite Images on a Global Scale
Authors: Amy Zhang, Xian-Ming Liu, Andreas Gros, Tobias Tiecke
Date: 2017-07-27 00:00:00
Tasks: Density Estimation

In the last several years, remote sensing technology has opened up the
possibility of performing large scale building detection from satellite
imagery. Our work is some of the first to create population density maps from
building detection on a large scale. The scale of our work on population
density estimation via high resolution satellite images raises many issues,
that we will address in this paper. The first was data acquisition. Labeling
buildings from satellite images is a hard problem, one where we found our
labelers to only be about 85% accurate at. There is a tradeoff of quantity vs.
quality of labels, so we designed two separate policies for labels meant for
training sets and those meant for test sets, since our requirements of the two
set types are quite different. We also trained weakly supervised footprint
detection models with the classification labels, and semi-supervised approaches
with a small number of pixel-level labels, which are very expensive to procure.
Anisotropic EM Segmentation by 3D Affinity Learning and Agglomeration
Authors: Toufiq Parag, Fabian Tschopp, William Grisaitis, Srinivas C. Turaga, Xuewen Zhang, Brian Matejek, Lee Kamentsky, Jeff W. Lichtman, Hanspeter Pfister
Date: 2017-07-27 00:00:00
Tasks: Segmentation

The field of connectomics has recently produced neuron wiring diagrams from
relatively large brain regions from multiple animals. Most of these neural
reconstructions were computed from isotropic (e.g., FIBSEM) or near isotropic
(e.g., SBEM) data. In spite of the remarkable progress on algorithms in recent
years, automatic dense reconstruction from anisotropic data remains a challenge
for the connectomics community. One significant hurdle in the segmentation of
anisotropic data is the difficulty in generating a suitable initial
over-segmentation. In this study, we present a segmentation method for
anisotropic EM data that agglomerates a 3D over-segmentation computed from the
3D affinity prediction. A 3D U-net is trained to predict 3D affinities by the
MALIS approach. Experiments on multiple datasets demonstrates the strength and
robustness of the proposed method for anisotropic EM segmentation.
A Family of Metrics for Clustering Algorithms
Authors: Clark Alexander, Sofya Akhmametyeva
Date: 2017-07-27 00:00:00
Tasks: Clustering, Sensitivity

We give the motivation for scoring clustering algorithms and a metric $M : A
\rightarrow \mathbb{N}$ from the set of clustering algorithms to the natural
numbers which we realize as \begin{equation} M(A) = \sum_i \alpha_i |f_i -
\beta_i|^{w_i} \end{equation} where $\alpha_i,\beta_i,w_i$ are parameters used
for scoring the feature $f_i$, which is computed empirically.. We give a method
by which one can score features such as stability, noise sensitivity, etc and
derive the necessary parameters. We conclude by giving a sample set of scores.
Robust Incremental Neural Semantic Graph Parsing
Authors: Jan Buys, Phil Blunsom
Date: 2017-04-24 00:00:00
Proceeding: ACL 2017 7
Tasks: Abstract Meaning Representation, AMR Parsing, Decoder, GPU

Parsing sentences to linguistically-expressive semantic representations is a
key goal of Natural Language Processing. Yet statistical parsing has focused
almost exclusively on bilexical dependencies or domain-specific logical forms.
We propose a neural encoder-decoder transition-based parser which is the first
full-coverage semantic graph parser for Minimal Recursion Semantics (MRS). The
model architecture uses stack-based embedding features, predicting graphs
jointly with unlexicalized predicates and their token alignments. Our parser is
more accurate than attention-based baselines on MRS, and on an additional
Abstract Meaning Representation (AMR) benchmark, and GPU batch processing makes
it an order of magnitude faster than a high-precision grammar-based parser.
Further, the 86.69% Smatch score of our MRS parser is higher than the
upper-bound on AMR parsing, making MRS an attractive choice as a semantic
representation.
Batch Reinforcement Learning on the Industrial Benchmark: First Experiences
Authors: Daniel Hein, Steffen Udluft, Michel Tokic, Alexander Hentschel, Thomas A. Runkler, Volkmar Sterzing
Date: 2017-05-20 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The Particle Swarm Optimization Policy (PSO-P) has been recently introduced
and proven to produce remarkable results on interacting with academic
reinforcement learning benchmarks in an off-policy, batch-based setting. To
further investigate the properties and feasibility on real-world applications,
this paper investigates PSO-P on the so-called Industrial Benchmark (IB), a
novel reinforcement learning (RL) benchmark that aims at being realistic by
including a variety of aspects found in industrial applications, like
continuous state and action spaces, a high dimensional, partially observable
state space, delayed effects, and complex stochasticity. The experimental
results of PSO-P on IB are compared to results of closed-form control policies
derived from the model-based Recurrent Control Neural Network (RCNN) and the
model-free Neural Fitted Q-Iteration (NFQ). Experiments show that PSO-P is not
only of interest for academic benchmarks, but also for real-world industrial
applications, since it also yielded the best performing policy in our IB
setting. Compared to other well established RL techniques, PSO-P produced
outstanding results in performance and robustness, requiring only a relatively
low amount of effort in finding adequate parameters or making complex design
decisions.
Providing Self-Aware Systems with Reflexivity
Authors: Alessandro Valitutti, Giuseppe Trautteur
Date: 2017-07-27 00:00:00

We propose a new type of self-aware systems inspired by ideas from
higher-order theories of consciousness. First, we discussed the crucial
distinction between introspection and reflexion. Then, we focus on
computational reflexion as a mechanism by which a computer program can inspect
its own code at every stage of the computation. Finally, we provide a formal
definition and a proof-of-concept implementation of computational reflexion,
viewed as an enriched form of program interpretation and a way to dynamically
"augment" a computational process.
Non-Count Symmetries in Boolean & Multi-Valued Prob. Graphical Models
Authors: Ankit Anand, Ritesh Noothigattu, Parag Singla, Mausam
Date: 2017-07-27 00:00:00

Lifted inference algorithms commonly exploit symmetries in a probabilistic
graphical model (PGM) for efficient inference. However, existing algorithms for
Boolean-valued domains can identify only those pairs of states as symmetric, in
which the number of ones and zeros match exactly (count symmetries). Moreover,
algorithms for lifted inference in multi-valued domains also compute a
multi-valued extension of count symmetries only. These algorithms miss many
symmetries in a domain. In this paper, we present first algorithms to compute
non-count symmetries in both Boolean-valued and multi-valued domains. Our
methods can also find symmetries between multi-valued variables that have
different domain cardinalities. The key insight in the algorithms is that they
change the unit of symmetry computation from a variable to a variable-value
(VV) pair. Our experiments find that exploiting these symmetries in MCMC can
obtain substantial computational gains over existing algorithms.
Deep Residual Learning for Weakly-Supervised Relation Extraction
Authors: Yi Yao Huang, William Yang Wang
Date: 2017-07-27 00:00:00
Proceeding: EMNLP 2017 9
Tasks: General Classification, Relation, Relation Extraction

Deep residual learning (ResNet) is a new method for training very deep neural
networks using identity map-ping for shortcut connections. ResNet has won the
ImageNet ILSVRC 2015 classification task, and achieved state-of-the-art
performances in many computer vision tasks. However, the effect of residual
learning on noisy natural language processing tasks is still not well
understood. In this paper, we design a novel convolutional neural network (CNN)
with residual learning, and investigate its impacts on the task of distantly
supervised noisy relation extraction. In contradictory to popular beliefs that
ResNet only works well for very deep networks, we found that even with 9 layers
of CNNs, using identity mapping could significantly improve the performance for
distantly-supervised relation extraction.