pwc-archive/papers-with-abstracts huggingface dataset:
Subspace Selection to Suppress Confounding Source Domain Information in AAM Transfer Learning
Authors: Azin Asgarian, Ahmed Bilal Ashraf, David Fleet, Babak Taati
Date: 2017-08-28 00:00:00
Tasks: Transfer Learning

Active appearance models (AAMs) are a class of generative models that have
seen tremendous success in face analysis. However, model learning depends on
the availability of detailed annotation of canonical landmark points. As a
result, when accurate AAM fitting is required on a different set of variations
(expression, pose, identity), a new dataset is collected and annotated. To
overcome the need for time consuming data collection and annotation, transfer
learning approaches have received recent attention. The goal is to transfer
knowledge from previously available datasets (source) to a new dataset
(target). We propose a subspace transfer learning method, in which we select a
subspace from the source that best describes the target space. We propose a
metric to compute the directional similarity between the source eigenvectors
and the target subspace. We show an equivalence between this metric and the
variance of target data when projected onto source eigenvectors. Using this
equivalence, we select a subset of source principal directions that capture the
variance in target data. To define our model, we augment the selected source
subspace with the target subspace learned from a handful of target examples. In
experiments done on six publicly available datasets, we show that our approach
outperforms the state of the art in terms of the RMS fitting error as well as
the percentage of test examples for which AAM fitting converges to the ground
truth.
Visual gesture variability between talkers in continuous visual speech
Authors: Helen L. Bear
Date: 2017-10-03 00:00:00
Tasks: Lipreading

Recent adoption of deep learning methods to the field of machine lipreading
research gives us two options to pursue to improve system performance. Either,
we develop end-to-end systems holistically or, we experiment to further our
understanding of the visual speech signal. The latter option is more difficult
but this knowledge would enable researchers to both improve systems and apply
the new knowledge to other domains such as speech therapy. One challenge in
lipreading systems is the correct labeling of the classifiers. These labels map
an estimated function between visemes on the lips and the phonemes uttered.
Here we ask if such maps are speaker-dependent? Prior work investigated
isolated word recognition from speaker-dependent (SD) visemes, we extend this
to continuous speech. Benchmarked against SD results, and the isolated words
performance, we test with RMAV dataset speakers and observe that with
continuous speech, the trajectory between visemes has a greater negative effect
on the speaker differentiation.
Visual speech recognition: aligning terminologies for better understanding
Authors: Helen L. Bear, Sarah Taylor
Date: 2017-10-03 00:00:00
Tasks: Lipreading, speech-recognition, Speech Recognition, Visual Speech Recognition

We are at an exciting time for machine lipreading. Traditional research
stemmed from the adaptation of audio recognition systems. But now, the computer
vision community is also participating. This joining of two previously
disparate areas with different perspectives on computer lipreading is creating
opportunities for collaborations, but in doing so the literature is
experiencing challenges in knowledge sharing due to multiple uses of terms and
phrases and the range of methods for scoring results.
  In particular we highlight three areas with the intention to improve
communication between those researching lipreading; the effects of
interchanging between speech reading and lipreading; speaker dependence across
train, validation, and test splits; and the use of accuracy, correctness,
errors, and varying units (phonemes, visemes, words, and sentences) to measure
system performance. We make recommendations as to how we can be more
consistent.
Decoding visemes: improving machine lipreading
Authors: Helen L. Bear
Date: 2017-10-03 00:00:00
Tasks: Clustering, General Classification, Lipreading, Robust classification, speech-recognition, Speech Recognition

Machine lipreading (MLR) is speech recognition from visual cues and a niche
research problem in speech processing & computer vision. Current challenges
fall into two groups: the content of the video, such as rate of speech or; the
parameters of the video recording e.g, video resolution. We show that HD video
is not needed to successfully lipread with a computer. The term "viseme" is
used in machine lipreading to represent a visual cue or gesture which
corresponds to a subgroup of phonemes where the phonemes are visually
indistinguishable. A phoneme is the smallest sound one can utter, because there
are more phonemes per viseme, maps between units show a many-to-one
relationship. Many maps have been presented, we compare these and our results
show Lee's is best. We propose a new method of speaker-dependent
phoneme-to-viseme maps and compare these to Lee's. Our results show the
sensitivity of phoneme clustering and we use our new knowledge to augment a
conventional MLR system. It has been observed in MLR, that classifiers need
training on test subjects to achieve accuracy. Thus machine lipreading is
highly speaker-dependent. Conversely speaker independence is robust
classification of non-training speakers. We investigate the dependence of
phoneme-to-viseme maps between speakers and show there is not a high
variability of visemes, but there is high variability in trajectory between
visemes of individual speakers with the same ground truth. This implies a
dependency upon the number of visemes within each set for each individual. We
show that prior phoneme-to-viseme maps rarely have enough visemes and the
optimal size, which varies by speaker, ranges from 11-35. Finally we decode
from visemes back to phonemes and into words. Our novel approach uses the
optimum range visemes within hierarchical training of phoneme classifiers and
demonstrates a significant increase in classification accuracy.
Dilated Convolutions for Modeling Long-Distance Genomic Dependencies
Authors: Ankit Gupta, Alexander M. Rush
Date: 2017-10-03 00:00:00

We consider the task of detecting regulatory elements in the human genome
directly from raw DNA. Past work has focused on small snippets of DNA, making
it difficult to model long-distance dependencies that arise from DNA's
3-dimensional conformation. In order to study long-distance dependencies, we
develop and release a novel dataset for a larger-context modeling task. Using
this new data set we model long-distance interactions using dilated
convolutional neural networks, and compare them to standard convolutions and
recurrent neural networks. We show that dilated convolutions are effective at
modeling the locations of regulatory markers in the human genome, such as
transcription factor binding sites, histone modifications, and DNAse
hypersensitivity sites.
Indexing the Event Calculus with Kd-trees to Monitor Diabetes
Authors: Stefano Bromuri, Albert Brugues de la Torre, Fabien Duboisson, Michael Schumacher
Date: 2017-10-03 00:00:00
Tasks: Time Series, Time Series Analysis

Personal Health Systems (PHS) are mobile solutions tailored to monitoring
patients affected by chronic non communicable diseases. A patient affected by a
chronic disease can generate large amounts of events. Type 1 Diabetic patients
generate several glucose events per day, ranging from at least 6 events per day
(under normal monitoring) to 288 per day when wearing a continuous glucose
monitor (CGM) that samples the blood every 5 minutes for several days. This is
a large number of events to monitor for medical doctors, in particular when
considering that they may have to take decisions concerning adjusting the
treatment, which may impact the life of the patients for a long time. Given the
need to analyse such a large stream of data, doctors need a simple approach
towards physiological time series that allows them to promptly transfer their
knowledge into queries to identify interesting patterns in the data. Achieving
this with current technology is not an easy task, as on one hand it cannot be
expected that medical doctors have the technical knowledge to query databases
and on the other hand these time series include thousands of events, which
requires to re-think the way data is indexed. In order to tackle the knowledge
representation and efficiency problem, this contribution presents the kd-tree
cached event calculus (\ceckd) an event calculus extension for knowledge
engineering of temporal rules capable to handle many thousands events produced
by a diabetic patient. \ceckd\ is built as a support to a graphical interface
to represent monitoring rules for diabetes type 1. In addition, the paper
evaluates the \ceckd\ with respect to the cached event calculus (CEC) to show
how indexing events using kd-trees improves scalability with respect to the
current state of the art.
A Survey on Optical Character Recognition System
Authors: Noman Islam, Zeeshan Islam, Nazia Noor
Date: 2017-10-03 00:00:00
Tasks: Optical Character Recognition, Optical Character Recognition (OCR), Survey

Optical Character Recognition (OCR) has been a topic of interest for many
years. It is defined as the process of digitizing a document image into its
constituent characters. Despite decades of intense research, developing OCR
with capabilities comparable to that of human still remains an open challenge.
Due to this challenging nature, researchers from industry and academic circles
have directed their attentions towards Optical Character Recognition. Over the
last few years, the number of academic laboratories and companies involved in
research on Character Recognition has increased dramatically. This research
aims at summarizing the research so far done in the field of OCR. It provides
an overview of different aspects of OCR and discusses corresponding proposals
aimed at resolving issues of OCR.
Person Re-Identification with Vision and Language
Authors: Fei Yan, Krystian Mikolajczyk, Josef Kittler
Date: 2017-10-03 00:00:00
Tasks: Language Modeling, Language Modelling, Person Re-Identification

In this paper we propose a new approach to person re-identification using
images and natural language descriptions. We propose a joint vision and
language model based on CCA and CNN architectures to match across the two
modalities as well as to enrich visual examples for which there are no language
descriptions. We also introduce new annotations in the form of natural language
descriptions for two standard Re-ID benchmarks, namely CUHK03 and VIPeR. We
perform experiments on these two datasets with techniques based on CNN,
hand-crafted features as well as LSTM for analysing visual and natural
description data. We investigate and demonstrate the advantages of using
natural language descriptions compared to attributes as well as CNN compared to
LSTM in the context of Re-ID. We show that the joint use of language and vision
can significantly improve the state-of-the-art performance on standard Re-ID
benchmarks.
Crowdsourcing Ground Truth for Medical Relation Extraction
Authors: Anca Dumitrache, Lora Aroyo, Chris Welty
Date: 2017-01-09 00:00:00
Tasks: Medical Relation Extraction, Relation, Relation Extraction

Cognitive computing systems require human labeled data for evaluation, and
often for training. The standard practice used in gathering this data minimizes
disagreement between annotators, and we have found this results in data that
fails to account for the ambiguity inherent in language. We have proposed the
CrowdTruth method for collecting ground truth through crowdsourcing, that
reconsiders the role of people in machine learning based on the observation
that disagreement between annotators provides a useful signal for phenomena
such as ambiguity in the text. We report on using this method to build an
annotated data set for medical relation extraction for the $cause$ and $treat$
relations, and how this data performed in a supervised training experiment. We
demonstrate that by modeling ambiguity, labeled data gathered from crowd
workers can (1) reach the level of quality of domain experts for this task
while reducing the cost, and (2) provide better training data at scale than
distant supervision. We further propose and validate new weighted measures for
precision, recall, and F-measure, that account for ambiguity in both human and
machine performance on this task.
HANDY: A Hybrid Association Rules Mining Approach for Network Layer Discovery of Services for Mobile Ad hoc Network
Authors: Noman Islam, Zubair A. Shaikh, Aqeel-ur-Rehman, Muhammad Shahab Siddiqui
Date: 2017-10-03 00:00:00

Mobile Ad hoc Network (MANET) is an infrastructure-less network formed
between a set of mobile nodes. The discovery of services in MANET is a
challenging job due to the unique properties of network. In this paper, a novel
service discovery framework called Hybrid Association Rules Based Network Layer
Discovery of Services for Ad hoc Networks (HANDY) has been proposed. HANDY
provides three major research contributions. At first, it adopts a cross-layer
optimized design for discovery of services that is based on simultaneous
discovery of services and corresponding routes. Secondly, it provides a
multi-level ontology-based approach to describe the services. This resolves the
issue of semantic interoperability among the service consumers in a scalable
fashion. Finally, to further optimize the performance of the discovery process,
HANDY recommends exploiting the inherent associations present among the
services. These associations are used in two ways. First, periodic service
advertisements are performed based on these associations. In addition, when a
response of a service discovery request is generated, correlated services are
also attached with the response. The proposed service discovery scheme has been
implemented in JIST/SWANS simulator. The results demonstrate that the proposed
modifications give rise to improvement in hit ratio of the service consumers
and latency of discovery process.
Learning to update Auto-associative Memory in Recurrent Neural Networks for Improving Sequence Memorization
Authors: Wei Zhang, Bo-Wen Zhou
Date: 2017-09-19 00:00:00
Tasks: Memorization, Representation Learning

Learning to remember long sequences remains a challenging task for recurrent
neural networks. Register memory and attention mechanisms were both proposed to
resolve the issue with either high computational cost to retain memory
differentiability, or by discounting the RNN representation learning towards
encoding shorter local contexts than encouraging long sequence encoding.
Associative memory, which studies the compression of multiple patterns in a
fixed size memory, were rarely considered in recent years. Although some recent
work tries to introduce associative memory in RNN and mimic the energy decay
process in Hopfield nets, it inherits the shortcoming of rule-based memory
updates, and the memory capacity is limited. This paper proposes a method to
learn the memory update rule jointly with task objective to improve memory
capacity for remembering long sequences. Also, we propose an architecture that
uses multiple such associative memory for more complex input encoding. We
observed some interesting facts when compared to other RNN architectures on
some well-studied sequence learning tasks.
Terahertz Security Image Quality Assessment by No-reference Model Observers
Authors: Menghan Hu, Xiongkuo Min, Guangtao Zhai, Wenhan Zhu, Yucheng Zhu, Zhaodi Wang, Xiaokang Yang, Guang Tian
Date: 2017-07-12 00:00:00
Tasks: Image Quality Assessment

To provide the possibility of developing objective image quality assessment
(IQA) algorithms for THz security images, we constructed the THz security image
database (THSID) including a total of 181 THz security images with the
resolution of 127*380. The main distortion types in THz security images were
first analyzed for the design of subjective evaluation criteria to acquire the
mean opinion scores. Subsequently, the existing no-reference IQA algorithms,
which were 5 opinion-aware approaches viz., NFERM, GMLF, DIIVINE, BRISQUE and
BLIINDS2, and 8 opinion-unaware approaches viz., QAC, SISBLIM, NIQE, FISBLIM,
CPBD, S3 and Fish_bb, were executed for the evaluation of the THz security
image quality. The statistical results demonstrated the superiority of Fish_bb
over the other testing IQA approaches for assessing the THz image quality with
PLCC (SROCC) values of 0.8925 (-0.8706), and with RMSE value of 0.3993. The
linear regression analysis and Bland-Altman plot further verified that the
Fish__bb could substitute for the subjective IQA. Nonetheless, for the
classification of THz security images, we tended to use S3 as a criterion for
ranking THz security image grades because of the relatively low false positive
rate in classifying bad THz image quality into acceptable category (24.69%).
Interestingly, due to the specific property of THz image, the average pixel
intensity gave the best performance than the above complicated IQA algorithms,
with the PLCC, SROCC and RMSE of 0.9001, -0.8800 and 0.3857, respectively. This
study will help the users such as researchers or security staffs to obtain the
THz security images of good quality. Currently, our research group is
attempting to make this research more comprehensive.
Decoding visemes: improving machine lipreading
Authors: Helen L. Bear, Richard Harvey
Date: 2017-10-03 00:00:00
Tasks: Classification, General Classification, Lipreading, Lip Reading

To undertake machine lip-reading, we try to recognise speech from a visual
signal. Current work often uses viseme classification supported by language
models with varying degrees of success. A few recent works suggest phoneme
classification, in the right circumstances, can outperform viseme
classification. In this work we present a novel two-pass method of training
phoneme classifiers which uses previously trained visemes in the first pass.
With our new training algorithm, we show classification performance which
significantly improves on previous lip-reading results.
Finding phonemes: improving machine lip-reading
Authors: Helen L. Bear, Richard W. Harvey, Yuxuan Lan
Date: 2017-10-03 00:00:00
Tasks: Lip Reading, Phoneme Recognition

In machine lip-reading there is continued debate and research around the
correct classes to be used for recognition. In this paper we use a structured
approach for devising speaker-dependent viseme classes, which enables the
creation of a set of phoneme-to-viseme maps where each has a different quantity
of visemes ranging from two to 45. Viseme classes are based upon the mapping of
articulated phonemes, which have been confused during phoneme recognition, into
viseme groups. Using these maps, with the LiLIR dataset, we show the effect of
changing the viseme map size in speaker-dependent machine lip-reading, measured
by word recognition correctness and so demonstrate that word recognition with
phoneme classifiers is not just possible, but often better than word
recognition with viseme classifiers. Furthermore, there are intermediate units
between visemes and phonemes which are better still.
Speaker-independent machine lip-reading with speaker-dependent viseme classifiers
Authors: Helen L. Bear, Stephen J. Cox, Richard W. Harvey
Date: 2017-10-03 00:00:00
Tasks: Clustering, Lip Reading

In machine lip-reading, which is identification of speech from visual-only
information, there is evidence to show that visual speech is highly dependent
upon the speaker [1]. Here, we use a phoneme-clustering method to form new
phoneme-to-viseme maps for both individual and multiple speakers. We use these
maps to examine how similarly speakers talk visually. We conclude that broadly
speaking, speakers have the same repertoire of mouth gestures, where they
differ is in the use of the gestures.
Isotropic and Steerable Wavelets in N Dimensions. A multiresolution analysis framework for ITK
Authors: Pablo Hernandez-Cerdan
Date: 2017-10-03 00:00:00
Tasks: Denoising

This document describes the implementation of the external module
ITKIsotropicWavelets, a multiresolution (MRA) analysis framework using
isotropic and steerable wavelets in the frequency domain. This framework
provides the backbone for state of the art filters for denoising, feature
detection or phase analysis in N-dimensions. It focus on reusability, and
highly decoupled modules for easy extension and implementation of new filters,
and it contains a filter for multiresolution phase analysis,
  The backbone of the multi-scale analysis is provided by an isotropic
band-limited wavelet pyramid, and the detection of directional features is
provided by coupling the pyramid with a generalized Riesz transform. The
generalized Riesz transform of order N behaves like a smoothed version of the
Nth order derivatives of the signal. Also, it is steerable: its components
impulse responses can be rotated to any spatial orientation, reducing
computation time when detecting directional features.
Towards an Inferential Lexicon of Event Selecting Predicates for French
Authors: Ingrid Falk, Fabienne Martin
Date: 2017-10-03 00:00:00
Proceeding: WS 2017 1

We present a manually constructed seed lexicon encoding the inferential
profiles of French event selecting predicates across different uses. The
inferential profile (Karttunen, 1971a) of a verb is designed to capture the
inferences triggered by the use of this verb in context. It reflects the
influence of the clause-embedding verb on the factuality of the event described
by the embedded clause. The resource developed provides evidence for the
following three hypotheses: (i) French implicative verbs have an aspect
dependent profile (their inferential profile varies with outer aspect), while
factive verbs have an aspect independent profile (they keep the same
inferential profile with both imperfective and perfective aspect); (ii)
implicativity decreases with imperfective aspect: the inferences triggered by
French implicative verbs combined with perfective aspect are often weakened
when the same verbs are combined with imperfective aspect; (iii) implicativity
decreases with an animate (deep) subject: the inferences triggered by a verb
which is implicative with an inanimate subject are weakened when the same verb
is used with an animate subject. The resource additionally shows that verbs
with different inferential profiles display clearly distinct sub-categorisation
patterns. In particular, verbs that have both factive and implicative readings
are shown to prefer infinitival clauses in their implicative reading, and
tensed clauses in their factive reading.
Boosting Neural Machine Translation
Authors: Dakun Zhang, Jungi Kim, Josep Crego, Jean Senellart
Date: 2016-12-19 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Machine Translation, NMT, Translation

Training efficiency is one of the main problems for Neural Machine
Translation (NMT). Deep networks need for very large data as well as many
training iterations to achieve state-of-the-art performance. This results in
very high computation cost, slowing down research and industrialisation. In
this paper, we propose to alleviate this problem with several training methods
based on data boosting and bootstrap with no modifications to the neural
network. It imitates the learning process of humans, which typically spend more
time when learning "difficult" concepts than easier ones. We experiment on an
English-French translation task showing accuracy improvements of up to 1.63
BLEU while saving 20% of training time.