pwc-archive/papers-with-abstracts huggingface dataset:
Recognition of Text Image Using Multilayer Perceptron
Authors: Singh Vijendra, Nisha Vasudeva, Hem Jyotsana Parashar
Date: 2016-12-02 00:00:00
Tasks: Optical Character Recognition, Optical Character Recognition (OCR)

The biggest challenge in the field of image processing is to recognize
documents both in printed and handwritten format. Optical Character Recognition
OCR is a type of document image analysis where scanned digital image that
contains either machine printed or handwritten script input into an OCR
software engine and translating it into an editable machine readable digital
text format. A Neural network is designed to model the way in which the brain
performs a particular task or function of interest: The neural network is
simulated in software on a digital computer. Character Recognition refers to
the process of converting printed Text documents into translated Unicode Text.
The printed documents available in the form of books, papers, magazines, etc.
are scanned using standard scanners which produce an image of the scanned
document. Lines are identifying by an algorithm where we identify top and
bottom of line. Then in each line character boundaries are calculated by an
algorithm then using these calculation, characters is isolated from the image
and then we classify each character by basic back propagation. Each image
character is comprised of 30*20 pixels. We have used the Back propagation
Neural Network for efficient recognition where the errors were corrected
through back propagation and rectified neuron values were transmitted by
feed-forward method in the neural network of multiple layers.
A temporal model for multiple sclerosis course evolution
Authors: Samuele Fiorini, Andrea Tacchino, Giampaolo Brichetto, Alessandro Verri, Annalisa Barla
Date: 2016-12-02 00:00:00
Tasks: Binary Classification, General Classification, model, regression

Multiple Sclerosis is a degenerative condition of the central nervous system
that affects nearly 2.5 million of individuals in terms of their physical,
cognitive, psychological and social capabilities. Researchers are currently
investigating on the use of patient reported outcome measures for the
assessment of impact and evolution of the disease on the life of the patients.
To date, a clear understanding on the use of such measures to predict the
evolution of the disease is still lacking. In this work we resort to
regularized machine learning methods for binary classification and multiple
output regression. We propose a pipeline that can be used to predict the
disease progression from patient reported measures. The obtained model is
tested on a data set collected from an ongoing clinical research project.
Predictive Clinical Decision Support System with RNN Encoding and Tensor Decoding
Authors: Yinchong Yang, Peter A. Fasching, Markus Wallwiener, Tanja N. Fehm, Sara Y. Brucker, Volker Tresp
Date: 2016-12-02 00:00:00
Tasks: BIG-bench Machine Learning, Decoder

With the introduction of the Electric Health Records, large amounts of
digital data become available for analysis and decision support. When
physicians are prescribing treatments to a patient, they need to consider a
large range of data variety and volume, making decisions increasingly complex.
Machine learning based Clinical Decision Support systems can be a solution to
the data challenges. In this work we focus on a class of decision support in
which the physicians' decision is directly predicted. Concretely, the model
would assign higher probabilities to decisions that it presumes the physician
are more likely to make. Thus the CDS system can provide physicians with
rational recommendations. We also address the problem of correlation in target
features: Often a physician is required to make multiple (sub-)decisions in a
block, and that these decisions are mutually dependent. We propose a solution
to the target correlation problem using a tensor factorization model. In order
to handle the patients' historical information as sequential data, we apply the
so-called Encoder-Decoder-Framework which is based on Recurrent Neural Networks
(RNN) as encoders and a tensor factorization model as a decoder, a combination
which is novel in machine learning. With experiments with real-world datasets
we show that the proposed model does achieve better prediction performances.
Safety-Aware Robot Damage Recovery Using Constrained Bayesian Optimization and Simulated Priors
Authors: Vaios Papaspyros, Konstantinos Chatzilygeroudis, Vassilis Vassiliades, Jean-Baptiste Mouret
Date: 2016-11-28 00:00:00
Tasks: Bayesian Optimization

The recently introduced Intelligent Trial-and-Error (IT&E) algorithm showed
that robots can adapt to damage in a matter of a few trials. The success of
this algorithm relies on two components: prior knowledge acquired through
simulation with an intact robot, and Bayesian optimization (BO) that operates
on-line, on the damaged robot. While IT&E leads to fast damage recovery, it
does not incorporate any safety constraints that prevent the robot from
attempting harmful behaviors. In this work, we address this limitation by
replacing the BO component with a constrained BO procedure. We evaluate our
approach on a simulated damaged humanoid robot that needs to crawl as fast as
possible, while performing as few unsafe trials as possible. We compare our new
"safety-aware IT&E" algorithm to IT&E and a multi-objective version of IT&E in
which the safety constraints are dealt as separate objectives. Our results show
that our algorithm outperforms the other approaches, both in crawling speed
within the safe regions and number of unsafe trials.
SyncSpecCNN: Synchronized Spectral CNN for 3D Shape Segmentation
Authors: Li Yi, Hao Su, Xingwen Guo, Leonidas Guibas
Date: 2016-12-02 00:00:00
Proceeding: CVPR 2017 7
Tasks: 3D Part Segmentation

In this paper, we study the problem of semantic annotation on 3D models that
are represented as shape graphs. A functional view is taken to represent
localized information on graphs, so that annotations such as part segment or
keypoint are nothing but 0-1 indicator vertex functions. Compared with images
that are 2D grids, shape graphs are irregular and non-isomorphic data
structures. To enable the prediction of vertex functions on them by
convolutional neural networks, we resort to spectral CNN method that enables
weight sharing by parameterizing kernels in the spectral domain spanned by
graph laplacian eigenbases. Under this setting, our network, named SyncSpecCNN,
strive to overcome two key challenges: how to share coefficients and conduct
multi-scale analysis in different parts of the graph for a single shape, and
how to share information across related but different shapes that may be
represented by very different graphs. Towards these goals, we introduce a
spectral parameterization of dilated convolutional kernels and a spectral
transformer network. Experimentally we tested our SyncSpecCNN on various tasks,
including 3D shape part segmentation and 3D keypoint prediction.
State-of-the-art performance has been achieved on all benchmark datasets.
Globally Consistent Multi-People Tracking using Motion Patterns
Authors: Andrii Maksai, Xinchao Wang, Francois Fleuret, Pascal Fua
Date: 2016-12-02 00:00:00

Many state-of-the-art approaches to people tracking rely on detecting them in
each frame independently, grouping detections into short but reliable
trajectory segments, and then further grouping them into full trajectories.
This grouping typically relies on imposing local smoothness constraints but
almost never on enforcing more global constraints on the trajectories. In this
paper, we propose an approach to imposing global consistency by first inferring
behavioral patterns from the ground truth and then using them to guide the
tracking algorithm. When used in conjunction with several state-of-the-art
algorithms, this further increases their already good performance. Furthermore,
we propose an unsupervised scheme that yields almost similar improvements
without the need for ground truth.
BASS Net: Band-Adaptive Spectral-Spatial Feature Learning Neural Network for Hyperspectral Image Classification
Authors: Anirban Santara, Kaustubh Mani, Pranoot Hatwar, Ankit Singh, Ankur Garg, Kirti Padia, Pabitra Mitra
Date: 2016-12-01 00:00:00
Tasks: Classification, Deep Learning, General Classification, Hyperspectral Image Classification, image-classification, Image Classification

Deep learning based landcover classification algorithms have recently been
proposed in literature. In hyperspectral images (HSI) they face the challenges
of large dimensionality, spatial variability of spectral signatures and
scarcity of labeled data. In this article we propose an end-to-end deep
learning architecture that extracts band specific spectral-spatial features and
performs landcover classification. The architecture has fewer independent
connection weights and thus requires lesser number of training data. The method
is found to outperform the highest reported accuracies on popular hyperspectral
image data sets.
Communication Lower Bounds for Distributed Convex Optimization: Partition Data on Features
Authors: Zihao Chen, Luo Luo, Zhihua Zhang
Date: 2016-12-02 00:00:00

Recently, there has been an increasing interest in designing distributed
convex optimization algorithms under the setting where the data matrix is
partitioned on features. Algorithms under this setting sometimes have many
advantages over those under the setting where data is partitioned on samples,
especially when the number of features is huge. Therefore, it is important to
understand the inherent limitations of these optimization problems. In this
paper, with certain restrictions on the communication allowed in the
procedures, we develop tight lower bounds on communication rounds for a broad
class of non-incremental algorithms under this setting. We also provide a lower
bound on communication rounds for a class of (randomized) incremental
algorithms.
Learning to Search on Manifolds for 3D Pose Estimation of Articulated Objects
Authors: Yu Zhang, Chi Xu, Li Cheng
Date: 2016-12-02 00:00:00
Tasks: 3D Pose Estimation, Pose Estimation, Structured Prediction

This paper focuses on the challenging problem of 3D pose estimation of a
diverse spectrum of articulated objects from single depth images. A novel
structured prediction approach is considered, where 3D poses are represented as
skeletal models that naturally operate on manifolds. Given an input depth
image, the problem of predicting the most proper articulation of underlying
skeletal model is thus formulated as sequentially searching for the optimal
skeletal configuration. This is subsequently addressed by convolutional neural
nets trained end-to-end to render sequential prediction of the joint locations
as regressing a set of tangent vectors of the underlying manifolds. Our
approach is examined on various articulated objects including human hand,
mouse, and fish benchmark datasets. Empirically it is shown to deliver highly
competitive performance with respect to the state-of-the-arts, while operating
in real-time (over 30 FPS).
An extended MABAC for multi-attribute decision making using trapezoidal interval type-2 fuzzy numbers
Authors: Jagannath Roy, Ananta Ranjan, Animesh Debnath, Samarjit Kar
Date: 2016-07-05 00:00:00
Tasks: Attribute, Decision Making

In this paper, we attempt to extend Multi Attributive Border Approximation
area Comparison (MABAC) approach for multi-attribute decision making (MADM)
problems based on type-2 fuzzy sets (IT2FSs). As a special case of IT2FSs
interval type-2 trapezoidal fuzzy numbers (IT2TrFNs) are adopted here to deal
with uncertainties present in many practical evaluation and selection problems.
A systematic description of MABAC based on IT2TrFNs is presented in the current
study. The validity and feasibility of the proposed method are illustrated by a
practical example of selecting the most suitable candidate for a software
company which is heading to hire a system analysis engineer based on few
attributes. Finally, a comparison with two other existing MADM methods is
described.
Neural Word Segmentation Learning for Chinese
Authors: Deng Cai, Hai Zhao
Date: 2016-06-14 00:00:00
Proceeding: ACL 2016 8
Tasks: Chinese Word Segmentation, Feature Engineering, Segmentation

Most previous approaches to Chinese word segmentation formalize this problem
as a character-based sequence labeling task where only contextual information
within fixed sized local windows and simple interactions between adjacent tags
can be captured. In this paper, we propose a novel neural framework which
thoroughly eliminates context windows and can utilize complete segmentation
history. Our model employs a gated combination neural network over characters
to produce distributed representations of word candidates, which are then given
to a long short-term memory (LSTM) language scoring model. Experiments on the
benchmark datasets show that without the help of feature engineering as most
existing approaches, our models achieve competitive or better performances with
previous state-of-the-art methods.
A Novel Framework based on SVDD to Classify Water Saturation from Seismic Attributes
Authors: Soumi Chaki, Akhilesh Kumar Verma, Aurobinda Routray, William K. Mohanty, Mamata Jenamani
Date: 2016-12-02 00:00:00
Tasks: Classification, General Classification

Water saturation is an important property in reservoir engineering domain.
Thus, satisfactory classification of water saturation from seismic attributes
is beneficial for reservoir characterization. However, diverse and non-linear
nature of subsurface attributes makes the classification task difficult. In
this context, this paper proposes a generalized Support Vector Data Description
(SVDD) based novel classification framework to classify water saturation into
two classes (Class high and Class low) from three seismic attributes seismic
impedance, amplitude envelop, and seismic sweetness. G-metric means and program
execution time are used to quantify the performance of the proposed framework
along with established supervised classifiers. The documented results imply
that the proposed framework is superior to existing classifiers. The present
study is envisioned to contribute in further reservoir modeling.
Development of a hybrid learning system based on SVM, ANFIS and domain knowledge: DKFIS
Authors: Soumi Chaki, Aurobinda Routray, William K. Mohanty, Mamata Jenamani
Date: 2016-12-02 00:00:00
Tasks: Prediction

This paper presents the development of a hybrid learning system based on
Support Vector Machines (SVM), Adaptive Neuro-Fuzzy Inference System (ANFIS)
and domain knowledge to solve prediction problem. The proposed two-stage Domain
Knowledge based Fuzzy Information System (DKFIS) improves the prediction
accuracy attained by ANFIS alone. The proposed framework has been implemented
on a noisy and incomplete dataset acquired from a hydrocarbon field located at
western part of India. Here, oil saturation has been predicted from four
different well logs i.e. gamma ray, resistivity, density, and clay volume. In
the first stage, depending on zero or near zero and non-zero oil saturation
levels the input vector is classified into two classes (Class 0 and Class 1)
using SVM. The classification results have been further fine-tuned applying
expert knowledge based on the relationship among predictor variables i.e. well
logs and target variable - oil saturation. Second, an ANFIS is designed to
predict non-zero (Class 1) oil saturation values from predictor logs. The
predicted output has been further refined based on expert knowledge. It is
apparent from the experimental results that the expert intervention with
qualitative judgment at each stage has rendered the prediction into the
feasible and realistic ranges. The performance analysis of the prediction in
terms of four performance metrics such as correlation coefficient (CC), root
mean square error (RMSE), and absolute error mean (AEM), scatter index (SI) has
established DKFIS as a useful tool for reservoir characterization.
A novel multiclassSVM based framework to classify lithology from well logs: a real-world application
Authors: Soumi Chaki, Aurobinda Routray, William K. Mohanty, Mamata Jenamani
Date: 2016-12-02 00:00:00
Tasks: Classification, General Classification, Sand

Support vector machines (SVMs) have been recognized as a potential tool for
supervised classification analyses in different domains of research. In
essence, SVM is a binary classifier. Therefore, in case of a multiclass
problem, the problem is divided into a series of binary problems which are
solved by binary classifiers, and finally the classification results are
combined following either the one-against-one or one-against-all strategies. In
this paper, an attempt has been made to classify lithology using a multiclass
SVM based framework using well logs as predictor variables. Here, the lithology
is classified into four classes such as sand, shaly sand, sandy shale and shale
based on the relative values of sand and shale fractions as suggested by an
expert geologist. The available dataset consisting well logs (gamma ray,
neutron porosity, density, and P-sonic) and class information from four closely
spaced wells from an onshore hydrocarbon field is divided into training and
testing sets. We have used one-against-all strategy to combine the results of
multiple binary classifiers. The reported results established the superiority
of multiclass SVM compared to other classifiers in terms of classification
accuracy. The selection of kernel function and associated parameters has also
been investigated here. It can be envisaged from the results achieved in this
study that the proposed framework based on multiclass SVM can further be used
to solve classification problems. In future research endeavor, seismic
attributes can be introduced in the framework to classify the lithology
throughout a study area from seismic inputs.
A One class Classifier based Framework using SVDD : Application to an Imbalanced Geological Dataset
Authors: Soumi Chaki, Akhilesh Kumar Verma, Aurobinda Routray, William K. Mohanty, Mamata Jenamani
Date: 2016-12-02 00:00:00
Tasks: Classification, General Classification, One-Class Classification, One-class classifier

Evaluation of hydrocarbon reservoir requires classification of petrophysical
properties from available dataset. However, characterization of reservoir
attributes is difficult due to the nonlinear and heterogeneous nature of the
subsurface physical properties. In this context, present study proposes a
generalized one class classification framework based on Support Vector Data
Description (SVDD) to classify a reservoir characteristic water saturation into
two classes (Class high and Class low) from four logs namely gamma ray, neutron
porosity, bulk density, and P sonic using an imbalanced dataset. A comparison
is carried out among proposed framework and different supervised classification
algorithms in terms of g metric means and execution time. Experimental results
show that proposed framework has outperformed other classifiers in terms of
these performance evaluators. It is envisaged that the classification analysis
performed in this study will be useful in further reservoir modeling.
Active Search for Sparse Signals with Region Sensing
Authors: Yifei Ma, Roman Garnett, Jeff Schneider
Date: 2016-12-02 00:00:00
Tasks: Bayesian Optimization, Compressive Sensing, Multi-Armed Bandits

Autonomous systems can be used to search for sparse signals in a large space;
e.g., aerial robots can be deployed to localize threats, detect gas leaks, or
respond to distress calls. Intuitively, search algorithms may increase
efficiency by collecting aggregate measurements summarizing large contiguous
regions. However, most existing search methods either ignore the possibility of
such region observations (e.g., Bayesian optimization and multi-armed bandits)
or make strong assumptions about the sensing mechanism that allow each
measurement to arbitrarily encode all signals in the entire environment (e.g.,
compressive sensing). We propose an algorithm that actively collects data to
search for sparse signals using only noisy measurements of the average values
on rectangular regions (including single points), based on the greedy
maximization of information gain. We analyze our algorithm in 1d and show that
it requires $\tilde{O}(\frac{n}{\mu^2}+k^2)$ measurements to recover all of $k$
signal locations with small Bayes error, where $\mu$ and $n$ are the signal
strength and the size of the search space, respectively. We also show that
active designs can be fundamentally more efficient than passive designs with
region sensing, contrasting with the results of Arias-Castro, Candes, and
Davenport (2013). We demonstrate the empirical performance of our algorithm on
a search problem using satellite image data and in high dimensions.
Transductive Zero-Shot Action Recognition by Word-Vector Embedding
Authors: Xun Xu, Timothy Hospedales, Shaogang Gong
Date: 2015-11-13 00:00:00
Tasks: Action Recognition, Attribute, Temporal Action Localization, Zero-Shot Action Recognition, Zero-Shot Learning

The number of categories for action recognition is growing rapidly and it has
become increasingly hard to label sufficient training data for learning
conventional models for all categories. Instead of collecting ever more data
and labelling them exhaustively for all categories, an attractive alternative
approach is zero-shot learning" (ZSL). To that end, in this study we construct
a mapping between visual features and a semantic descriptor of each action
category, allowing new categories to be recognised in the absence of any visual
training data. Existing ZSL studies focus primarily on still images, and
attribute-based semantic representations. In this work, we explore word-vectors
as the shared semantic space to embed videos and category labels for ZSL action
recognition. This is a more challenging problem than existing ZSL of still
images and/or attributes, because the mapping between video spacetime features
of actions and the semantic space is more complex and harder to learn for the
purpose of generalising over any cross-category domain shift. To solve this
generalisation problem in ZSL action recognition, we investigate a series of
synergistic strategies to improve upon the standard ZSL pipeline. Most of these
strategies are transductive in nature which means access to testing data in the
training phase.
Shift-Reduce Constituent Parsing with Neural Lookahead Features
Authors: Jiangming Liu, Yue Zhang
Date: 2016-12-02 00:00:00
Proceeding: TACL 2017 1
Tasks: Sentence

Transition-based models can be fast and accurate for constituent parsing.
Compared with chart-based models, they leverage richer features by extracting
history information from a parser stack, which spans over non-local
constituents. On the other hand, during incremental parsing, constituent
information on the right hand side of the current word is not utilized, which
is a relative weakness of shift-reduce parsing. To address this limitation, we
leverage a fast neural model to extract lookahead features. In particular, we
build a bidirectional LSTM model, which leverages the full sentence information
to predict the hierarchy of constituents that each word starts and ends. The
results are then passed to a strong transition-based constituent parser as
lookahead features. The resulting parser gives 1.3% absolute improvement in WSJ
and 2.3% in CTB compared to the baseline, given the highest reported accuracies
for fully-supervised parsing.