pwc-archive/papers-with-abstracts huggingface dataset:
Multi-Level Recurrent Residual Networks for Action Recognition
Authors: Zhenxing Zheng, Gaoyun An, Qiuqi Ruan
Date: 2017-11-22 00:00:00
Tasks: Action Recognition, Temporal Action Localization

Most existing Convolutional Neural Networks(CNNs) used for action recognition
are either difficult to optimize or underuse crucial temporal information.
Inspired by the fact that the recurrent model consistently makes breakthroughs
in the task related to sequence, we propose a novel Multi-Level Recurrent
Residual Networks(MRRN) which incorporates three recognition streams. Each
stream consists of a Residual Networks(ResNets) and a recurrent model. The
proposed model captures spatiotemporal information by employing both
alternative ResNets to learn spatial representations from static frames and
stacked Simple Recurrent Units(SRUs) to model temporal dynamics. Three
distinct-level streams learned low-, mid-, high-level representations
independently are fused by computing a weighted average of their softmax scores
to obtain the complementary representations of the video. Unlike previous
models which boost performance at the cost of time complexity and space
complexity, our models have a lower complexity by employing shortcut connection
and are trained end-to-end with greater efficiency. MRRN displays significant
performance improvements compared to CNN-RNN framework baselines and obtains
comparable performance with the state-of-the-art, achieving 51.3% on HMDB-51
dataset and 81.9% on UCF-101 dataset although no additional data.
Context aware saliency map generation using semantic segmentation
Authors: Mahdi Ahmadi, Mohsen Hajabdollahi, Nader Karimi, Shadrokh Samavi
Date: 2017-12-31 00:00:00
Tasks: General Classification, image-classification, Image Classification, Saliency Detection, Semantic Segmentation

Saliency map detection, as a method for detecting important regions of an
image, is used in many applications such as image classification and
recognition. We propose that context detection could have an essential role in
image saliency detection. This requires extraction of high level features. In
this paper a saliency map is proposed, based on image context detection using
semantic segmentation as a high level feature. Saliency map from semantic
information is fused with color and contrast based saliency maps. The final
saliency map is then generated. Simulation results for Pascal-voc11 image
dataset show 99% accuracy in context detection. Also final saliency map
produced by our proposed method shows acceptable results in detecting salient
points.
Tree-Structured Neural Machine for Linguistics-Aware Sentence Generation
Authors: Ganbin Zhou, Ping Luo, Rongyu Cao, Yijun Xiao, Fen Lin, Bo Chen, Qing He
Date: 2017-04-30 00:00:00
Tasks: Decoder, Sentence

Different from other sequential data, sentences in natural language are
structured by linguistic grammars. Previous generative conversational models
with chain-structured decoder ignore this structure in human language and might
generate plausible responses with less satisfactory relevance and fluency. In
this study, we aim to incorporate the results from linguistic analysis into the
process of sentence generation for high-quality conversation generation.
Specifically, we use a dependency parser to transform each response sentence
into a dependency tree and construct a training corpus of sentence-tree pairs.
A tree-structured decoder is developed to learn the mapping from a sentence to
its tree, where different types of hidden states are used to depict the local
dependencies from an internal tree node to its children. For training
acceleration, we propose a tree canonicalization method, which transforms trees
into equivalent ternary trees. Then, with a proposed tree-structured search
method, the model is able to generate the most probable responses in the form
of dependency trees, which are finally flattened into sequences as the system
output. Experimental results demonstrate that the proposed X2Tree framework
outperforms baseline methods over 11.15% increase of acceptance ratio.
Neural Networks in Adversarial Setting and Ill-Conditioned Weight Space
Authors: Mayank Singh, Abhishek Sinha, Balaji Krishnamurthy
Date: 2018-01-03 00:00:00

Recently, Neural networks have seen a huge surge in its adoption due to their
ability to provide high accuracy on various tasks. On the other hand, the
existence of adversarial examples have raised suspicions regarding the
generalization capabilities of neural networks. In this work, we focus on the
weight matrix learnt by the neural networks and hypothesize that ill
conditioned weight matrix is one of the contributing factors in neural
network's susceptibility towards adversarial examples. For ensuring that the
learnt weight matrix's condition number remains sufficiently low, we suggest
using orthogonal regularizer. We show that this indeed helps in increasing the
adversarial accuracy on MNIST and F-MNIST datasets.
Density-Wise Two Stage Mammogram Classification using Texture Exploiting Descriptors
Authors: Aditya A. Shastri, Deepti Tamrakar, Kapil Ahuja
Date: 2017-01-15 00:00:00
Tasks: feature selection, General Classification, Image Retrieval, Retrieval, Vocal Bursts Valence Prediction

Breast cancer is becoming pervasive with each passing day. Hence, its early
detection is a big step in saving the life of any patient. Mammography is a
common tool in breast cancer diagnosis. The most important step here is
classification of mammogram patches as normal-abnormal and benign-malignant.
  Texture of a breast in a mammogram patch plays a significant role in these
classifications. We propose a variation of Histogram of Gradients (HOG) and
Gabor filter combination called Histogram of Oriented Texture (HOT) that
exploits this fact. We also revisit the Pass Band - Discrete Cosine Transform
(PB-DCT) descriptor that captures texture information well. All features of a
mammogram patch may not be useful. Hence, we apply a feature selection
technique called Discrimination Potentiality (DP). Our resulting descriptors,
DP-HOT and DP-PB-DCT, are compared with the standard descriptors.
  Density of a mammogram patch is important for classification, and has not
been studied exhaustively. The Image Retrieval in Medical Application (IRMA)
database from RWTH Aachen, Germany is a standard database that provides
mammogram patches, and most researchers have tested their frameworks only on a
subset of patches from this database. We apply our two new descriptors on all
images of the IRMA database for density wise classification, and compare with
the standard descriptors. We achieve higher accuracy than all of the existing
standard descriptors (more than 92%).
Improved EEG Event Classification Using Differential Energy
Authors: Amir Harati, Meysam Golmohammadi, Silvia Lopez, Iyad Obeid, Joseph Picone
Date: 2018-01-03 00:00:00
Tasks: Classification, EEG, Electroencephalogram (EEG), General Classification

Feature extraction for automatic classification of EEG signals typically
relies on time frequency representations of the signal. Techniques such as
cepstral-based filter banks or wavelets are popular analysis techniques in many
signal processing applications including EEG classification. In this paper, we
present a comparison of a variety of approaches to estimating and
postprocessing features. To further aid in discrimination of periodic signals
from aperiodic signals, we add a differential energy term. We evaluate our
approaches on the TUH EEG Corpus, which is the largest publicly available EEG
corpus and an exceedingly challenging task due to the clinical nature of the
data. We demonstrate that a variant of a standard filter bank-based approach,
coupled with first and second derivatives, provides a substantial reduction in
the overall error rate. The combination of differential energy and derivatives
produces a 24% absolute reduction in the error rate and improves our ability to
discriminate between signal events and background noise. This relatively simple
approach proves to be comparable to other popular feature extraction approaches
such as wavelets, but is much more computationally efficient.
Semi-automated Annotation of Signal Events in Clinical EEG Data
Authors: Scott Yang, Silvia Lopez, Meysam Golmohammadi, Iyad Obeid, Joseph Picone
Date: 2018-01-03 00:00:00
Tasks: Active Learning, BIG-bench Machine Learning, EEG, Electroencephalogram (EEG)

To be effective, state of the art machine learning technology needs large
amounts of annotated data. There are numerous compelling applications in
healthcare that can benefit from high performance automated decision support
systems provided by deep learning technology, but they lack the comprehensive
data resources required to apply sophisticated machine learning models.
Further, for economic reasons, it is very difficult to justify the creation of
large annotated corpora for these applications. Hence, automated annotation
techniques become increasingly important. In this study, we investigated the
effectiveness of using an active learning algorithm to automatically annotate a
large EEG corpus. The algorithm is designed to annotate six types of EEG
events. Two model training schemes, namely threshold-based and volume-based,
are evaluated. In the threshold-based scheme the threshold of confidence scores
is optimized in the initial training iteration, whereas for the volume-based
scheme only a certain amount of data is preserved after each iteration.
Recognition performance is improved 2% absolute and the system is capable of
automatically annotating previously unlabeled data. Given that the
interpretation of clinical EEG data is an exceedingly difficult task, this
study provides some evidence that the proposed method is a viable alternative
to expensive manual annotation.
An elementary derivation of the Chinese restaurant process from Sethuraman's stick-breaking process
Authors: Jeffrey W. Miller
Date: 2018-01-01 00:00:00

The Chinese restaurant process (CRP) and the stick-breaking process are the
two most commonly used representations of the Dirichlet process. However, the
usual proof of the connection between them is indirect, relying on abstract
properties of the Dirichlet process that are difficult for nonexperts to
verify. This short note provides a direct proof that the stick-breaking process
leads to the CRP, without using any measure theory. We also discuss how the
stick-breaking representation arises naturally from the CRP.
Recovery of Point Clouds on Surfaces: Application to Image Reconstruction
Authors: Sunrita Poddar, Mathews Jacob
Date: 2018-01-03 00:00:00
Tasks: Image Reconstruction

We introduce a framework for the recovery of points on a smooth surface in
high-dimensional space, with application to dynamic imaging. We assume the
surface to be the zero-level set of a bandlimited function. We show that the
exponential maps of the points on the surface satisfy annihilation relations,
implying that they lie in a finite dimensional subspace. We rely on nuclear
norm minimization of the maps to recover the points from noisy and undersampled
measurements. Since this direct approach suffers from the curse of
dimensionality, we introduce an iterative reweighted algorithm that uses the
"kernel trick". The resulting algorithm has similarities to iterative
algorithms used in graph signal processing (GSP); this framework can be seen as
a continuous domain alternative to discrete GSP theory. The use of the
algorithm in recovering free breathing and ungated cardiac data shows the
potential of this framework in practical applications.
Seeded Ising Model and Statistical Natures of Human Iris Templates
Authors: Song-Hwa Kwon, Hyeong In Choi, Sung Jin Lee, Nam-Sook Wee
Date: 2018-01-03 00:00:00
Tasks: Playing the Game of 2048

We propose a variant of Ising model, called the Seeded Ising Model, to model probabilistic nature of human iris templates. This model is an Ising model in which the values at certain lattice points are held fixed throughout Ising model evolution. Using this we show how to reconstruct the full iris template from partial information, and we show that about 1/6 of the given template is needed to recover almost all information content of the original one in the sense that the resulting Hamming distance is well within the range to assert correctly the identity of the subject. This leads us to propose the concept of effective statistical degree of freedom of iris templates and show it is about 1/6 of the total number of bits. In particular, for a template of $2048$ bits, its effective statistical degree of freedom is about $342$ bits, which coincides very well with the degree of freedom computed by the completely different method proposed by Daugman.
A Novel Feature Descriptor for Image Retrieval by Combining Modified Color Histogram and Diagonally Symmetric Co-occurrence Texture Pattern
Authors: Ayan Kumar Bhunia, Avirup Bhattacharyya, Prithaj Banerjee, Partha Pratim Roy, Subrahmanyam Murala
Date: 2018-01-03 00:00:00
Tasks: Image Retrieval, Retrieval

In this paper, we have proposed a novel feature descriptors combining color
and texture information collectively. In our proposed color descriptor
component, the inter-channel relationship between Hue (H) and Saturation (S)
channels in the HSV color space has been explored which was not done earlier.
We have quantized the H channel into a number of bins and performed the voting
with saturation values and vice versa by following a principle similar to that
of the HOG descriptor, where orientation of the gradient is quantized into a
certain number of bins and voting is done with gradient magnitude. This helps
us to study the nature of variation of saturation with variation in Hue and
nature of variation of Hue with the variation in saturation. The texture
component of our descriptor considers the co-occurrence relationship between
the pixels symmetric about both the diagonals of a 3x3 window. Our work is
inspired from the work done by Dubey et al.[1]. These two components, viz.
color and texture information individually perform better than existing texture
and color descriptors. Moreover, when concatenated the proposed descriptors
provide significant improvement over existing descriptors for content base
color image retrieval. The proposed descriptor has been tested for image
retrieval on five databases, including texture image databases - MIT VisTex
database and Salzburg texture database and natural scene databases Corel 1K,
Corel 5K and Corel 10K. The precision and recall values experimented on these
databases are compared with some state-of-art local patterns. The proposed
method provided satisfactory results from the experiments.
An Analysis of Two Common Reference Points for EEGs
Authors: Silvia Lopez, Aaron Gross, Scott Yang, Meysam Golmohammadi, Iyad Obeid, Joseph Picone
Date: 2018-01-03 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Vocal Bursts Valence Prediction

Clinical electroencephalographic (EEG) data varies significantly depending on
a number of operational conditions (e.g., the type and placement of electrodes,
the type of electrical grounding used). This investigation explores the
statistical differences present in two different referential montages: Linked
Ear (LE) and Averaged Reference (AR). Each of these accounts for approximately
45% of the data in the TUH EEG Corpus. In this study, we explore the impact
this variability has on machine learning performance. We compare the
statistical properties of features generated using these two montages, and
explore the impact of performance on our standard Hidden Markov Model (HMM)
based classification system. We show that a system trained on LE data
significantly outperforms one trained only on AR data (77.2% vs. 61.4%). We
also demonstrate that performance of a system trained on both data sets is
somewhat compromised (71.4% vs. 77.2%). A statistical analysis of the data
suggests that mean, variance and channel normalization should be considered.
However, cepstral mean subtraction failed to produce an improvement in
performance, suggesting that the impact of these statistical differences is
subtler.
The Temple University Hospital Seizure Detection Corpus
Authors: Vinit Shah, Eva von Weltin, Silvia Lopez, James Riley McHugh, Lily Veloso, Meysam Golmohammadi, Iyad Obeid, Joseph Picone
Date: 2018-01-03 00:00:00
Tasks: Descriptive, EEG, Electroencephalogram (EEG), Seizure Detection

We introduce the TUH EEG Seizure Corpus (TUSZ), which is the largest open
source corpus of its type, and represents an accurate characterization of
clinical conditions. In this paper, we describe the techniques used to develop
TUSZ, evaluate their effectiveness, and present some descriptive statistics on
the resulting corpus.
Optimizing Channel Selection for Seizure Detection
Authors: Vinit Shah, Meysam Golmohammadi, Saeedeh Ziyabari, Eva Von Weltin, Iyad Obeid, Joseph Picone
Date: 2018-01-03 00:00:00
Tasks: Artifact Detection, channel selection, EEG, Electroencephalogram (EEG), Seizure Detection

Interpretation of electroencephalogram (EEG) signals can be complicated by
obfuscating artifacts. Artifact detection plays an important role in the
observation and analysis of EEG signals. Spatial information contained in the
placement of the electrodes can be exploited to accurately detect artifacts.
However, when fewer electrodes are used, less spatial information is available,
making it harder to detect artifacts. In this study, we investigate the
performance of a deep learning algorithm, CNN-LSTM, on several channel
configurations. Each configuration was designed to minimize the amount of
spatial information lost compared to a standard 22-channel EEG. Systems using a
reduced number of channels ranging from 8 to 20 achieved sensitivities between
33% and 37% with false alarms in the range of [38, 50] per 24 hours. False
alarms increased dramatically (e.g., over 300 per 24 hours) when the number of
channels was further reduced. Baseline performance of a system that used all 22
channels was 39% sensitivity with 23 false alarms. Since the 22-channel system
was the only system that included referential channels, the rapid increase in
the false alarm rate as the number of channels was reduced underscores the
importance of retaining referential channels for artifact reduction. This
cautionary result is important because one of the biggest differences between
various types of EEGs administered is the type of referential channel used.
Gated Recurrent Networks for Seizure Detection
Authors: Meysam Golmohammadi, Saeedeh Ziyabari, Vinit Shah, Eva Von Weltin, Christopher Campbell, Iyad Obeid, Joseph Picone
Date: 2018-01-03 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Seizure Detection

Recurrent Neural Networks (RNNs) with sophisticated units that implement a
gating mechanism have emerged as powerful technique for modeling sequential
signals such as speech or electroencephalography (EEG). The latter is the focus
on this paper. A significant big data resource, known as the TUH EEG Corpus
(TUEEG), has recently become available for EEG research, creating a unique
opportunity to evaluate these recurrent units on the task of seizure detection.
In this study, we compare two types of recurrent units: long short-term memory
units (LSTM) and gated recurrent units (GRU). These are evaluated using a state
of the art hybrid architecture that integrates Convolutional Neural Networks
(CNNs) with RNNs. We also investigate a variety of initialization methods and
show that initialization is crucial since poorly initialized networks cannot be
trained. Furthermore, we explore regularization of these convolutional gated
recurrent networks to address the problem of overfitting. Our experiments
revealed that convolutional LSTM networks can achieve significantly better
performance than convolutional GRU networks. The convolutional LSTM
architecture with proper initialization and regularization delivers 30%
sensitivity at 6 false alarms per 24 hours.
Utilizing Semantic Visual Landmarks for Precise Vehicle Navigation
Authors: Varun Murali, Han-Pang Chiu, Supun Samarasekera, Rakesh, Kumar
Date: 2018-01-02 00:00:00
Tasks: Semantic Segmentation

This paper presents a new approach for integrating semantic information for
vision-based vehicle navigation. Although vision-based vehicle navigation
systems using pre-mapped visual landmarks are capable of achieving submeter
level accuracy in large-scale urban environment, a typical error source in this
type of systems comes from the presence of visual landmarks or features from
temporal objects in the environment, such as cars and pedestrians. We propose a
gated factor graph framework to use semantic information associated with visual
features to make decisions on outlier/ inlier computation from three
perspectives: the feature tracking process, the geo-referenced map building
process, and the navigation system using pre-mapped landmarks. The class
category that the visual feature belongs to is extracted from a pre-trained
deep learning network trained for semantic segmentation. The feasibility and
generality of our approach is demonstrated by our implementations on top of two
vision-based navigation systems. Experimental evaluations validate that the
injection of semantic information associated with visual landmarks using our
approach achieves substantial improvements in accuracy on GPS-denied navigation
solutions for large-scale urban scenarios
Heterogeneous domain adaptation: An unsupervised approach
Authors: Feng Liu, Guanquan Zhang, Jie Lu
Date: 2017-01-10 00:00:00
Tasks: Domain Adaptation, text-classification, Text Classification, Transfer Learning, Unsupervised Domain Adaptation

Domain adaptation leverages the knowledge in one domain - the source domain - to improve learning efficiency in another domain - the target domain. Existing heterogeneous domain adaptation research is relatively well-progressed, but only in situations where the target domain contains at least a few labeled instances. In contrast, heterogeneous domain adaptation with an unlabeled target domain has not been well-studied. To contribute to the research in this emerging field, this paper presents: (1) an unsupervised knowledge transfer theorem that guarantees the correctness of transferring knowledge; and (2) a principal angle-based metric to measure the distance between two pairs of domains: one pair comprises the original source and target domains and the other pair comprises two homogeneous representations of two domains. The theorem and the metric have been implemented in an innovative transfer model, called a Grassmann-Linear monotonic maps-geodesic flow kernel (GLG), that is specifically designed for heterogeneous unsupervised domain adaptation (HeUDA). The linear monotonic maps meet the conditions of the theorem and are used to construct homogeneous representations of the heterogeneous domains. The metric shows the extent to which the homogeneous representations have preserved the information in the original source and target domains. By minimizing the proposed metric, the GLG model learns the homogeneous representations of heterogeneous domains and transfers knowledge through these learned representations via a geodesic flow kernel. To evaluate the model, five public datasets were reorganized into ten HeUDA tasks across three applications: cancer detection, credit assessment, and text classification. The experiments demonstrate that the proposed model delivers superior performance over the existing baselines.