pwc-archive/papers-with-abstracts huggingface dataset:
Hierarchical Multi-scale Attention Networks for Action Recognition
Authors: Shi-Yang Yan, Jeremy S. Smith, Wenjin Lu, Bai-Ling Zhang
Date: 2017-08-25 00:00:00
Tasks: Action Recognition, Hard Attention, Temporal Action Localization

Recurrent Neural Networks (RNNs) have been widely used in natural language
processing and computer vision. Among them, the Hierarchical Multi-scale RNN
(HM-RNN), a kind of multi-scale hierarchical RNN proposed recently, can learn
the hierarchical temporal structure from data automatically. In this paper, we
extend the work to solve the computer vision task of action recognition.
However, in sequence-to-sequence models like RNN, it is normally very hard to
discover the relationships between inputs and outputs given static inputs. As a
solution, attention mechanism could be applied to extract the relevant
information from input thus facilitating the modeling of input-output
relationships. Based on these considerations, we propose a novel attention
network, namely Hierarchical Multi-scale Attention Network (HM-AN), by
combining the HM-RNN and the attention mechanism and apply it to action
recognition. A newly proposed gradient estimation method for stochastic
neurons, namely Gumbel-softmax, is exploited to implement the temporal boundary
detectors and the stochastic hard attention mechanism. To amealiate the
negative effect of sensitive temperature of the Gumbel-softmax, an adaptive
temperature training method is applied to better the system performance. The
experimental results demonstrate the improved effect of HM-AN over LSTM with
attention on the vision task. Through visualization of what have been learnt by
the networks, it can be observed that both the attention regions of images and
the hierarchical temporal structure can be captured by HM-AN.
An IoT Real-Time Biometric Authentication System Based on ECG Fiducial Extracted Features Using Discrete Cosine Transform
Authors: Ahmed F. Hussein, Abbas K. AlZubaidi, Ali Al-Bayaty, Qais A. Habash
Date: 2017-08-28 00:00:00

The conventional authentication technologies, like RFID tags and
authentication cards/badges, suffer from different weaknesses, therefore a
prompt replacement to use biometric method of authentication should be applied
instead. Biometrics, such as fingerprints, voices, and ECG signals, are unique
human characters that can be used for authentication processing. In this work,
we present an IoT real-time authentication system based on using extracted ECG
features to identify the unknown persons. The Discrete Cosine Transform (DCT)
is used as an ECG feature extraction, where it has better characteristics for
real-time system implementations. There are a substantial number of researches
with a high accuracy of authentication, but most of them ignore the real-time
capability of authenticating individuals. With the accuracy rate of 97.78% at
around 1.21 seconds of processing time, the proposed system is more suitable
for use in many applications that require fast and reliable authentication
processing demands.
Multi-Agent Q-Learning for Minimizing Demand-Supply Power Deficit in Microgrids
Authors: Raghuram Bharadwaj Diddigi, D. Sai Koti Reddy, Shalabh Bhatnagar
Date: 2017-08-25 00:00:00
Tasks: Q-Learning, Reinforcement Learning

We consider the problem of minimizing the difference in the demand and the
supply of power using microgrids. We setup multiple microgrids, that provide
electricity to a village. They have access to the batteries that can store
renewable power and also the electrical lines from the main grid. During each
time period, these microgrids need to take decision on the amount of renewable
power to be used from the batteries as well as the amount of power needed from
the main grid. We formulate this problem in the framework of Markov Decision
Process (MDP), similar to the one discussed in [1]. The power allotment to the
village from the main grid is fixed and bounded, whereas the renewable energy
generation is uncertain in nature. Therefore we adapt a distributed version of
the popular Reinforcement learning technique, Multi-Agent Q-Learning to the
problem. Finally, we also consider a variant of this problem where the cost of
power production at the main site is taken into consideration. In this scenario
the microgrids need to minimize the demand-supply deficit, while maintaining
the desired average cost of the power production.
Hyperprior on symmetric Dirichlet distribution
Authors: Jun Lu
Date: 2017-08-28 00:00:00

In this article we introduce how to put vague hyperprior on Dirichlet
distribution, and we update the parameter of it by adaptive rejection sampling
(ARS). Finally we analyze this hyperprior in an over-fitted mixture model by
some synthetic experiments.
ChainerCV: a Library for Deep Learning in Computer Vision
Authors: Yusuke Niitani, Toru Ogawa, Shunta Saito, Masaki Saito
Date: 2017-08-28 00:00:00
Tasks: Deep Learning, object-detection, Object Detection, Semantic Segmentation

Despite significant progress of deep learning in the field of computer
vision, there has not been a software library that covers these methods in a
unifying manner. We introduce ChainerCV, a software library that is intended to
fill this gap. ChainerCV supports numerous neural network models as well as
software components needed to conduct research in computer vision. These
implementations emphasize simplicity, flexibility and good software engineering
practices. The library is designed to perform on par with the results reported
in published papers and its tools can be used as a baseline for future research
in computer vision. Our implementation includes sophisticated models like
Faster R-CNN and SSD, and covers tasks such as object detection and semantic
segmentation.
The Importance of Automatic Syntactic Features in Vietnamese Named Entity Recognition
Authors: Thai-Hoang Pham, Phuong Le-Hong
Date: 2017-05-29 00:00:00
Proceeding: PACLIC 2017 11
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Word Embeddings

This paper presents a state-of-the-art system for Vietnamese Named Entity
Recognition (NER). By incorporating automatic syntactic features with word
embeddings as input for bidirectional Long Short-Term Memory (Bi-LSTM), our
system, although simpler than some deep learning architectures, achieves a much
better result for Vietnamese NER. The proposed method achieves an overall F1
score of 92.05% on the test set of an evaluation campaign, organized in late
2016 by the Vietnamese Language and Speech Processing (VLSP) community. Our
named entity recognition system outperforms the best previous systems for
Vietnamese NER by a large margin.
ByRDiE: Byzantine-resilient distributed coordinate descent for decentralized learning
Authors: Zhixiong Yang, Waheed U. Bajwa
Date: 2017-08-28 00:00:00

Distributed machine learning algorithms enable learning of models from datasets that are distributed over a network without gathering the data at a centralized location. While efficient distributed algorithms have been developed under the assumption of faultless networks, failures that can render these algorithms nonfunctional occur frequently in the real world. This paper focuses on the problem of Byzantine failures, which are the hardest to safeguard against in distributed algorithms. While Byzantine fault tolerance has a rich history, existing work does not translate into efficient and practical algorithms for high-dimensional learning in fully distributed (also known as decentralized) settings. In this paper, an algorithm termed Byzantine-resilient distributed coordinate descent (ByRDiE) is developed and analyzed that enables distributed learning in the presence of Byzantine failures. Theoretical analysis (convex settings) and numerical experiments (convex and nonconvex settings) highlight its usefulness for high-dimensional distributed learning in the presence of Byzantine failures.
Study of Set-Membership Kernel Adaptive Algorithms and Applications
Authors: R. C. de Lamare, André Flores
Date: 2017-08-27 00:00:00

Adaptive algorithms based on kernel structures have been a topic of
significant research over the past few years. The main advantage is that they
form a family of universal approximators, offering an elegant solution to
problems with nonlinearities. Nevertheless these methods deal with kernel
expansions, creating a growing structure also known as dictionary, whose size
depends on the number of new inputs. In this paper we derive the set-membership
kernel-based normalized least-mean square (SM-NKLMS) algorithm, which is
capable of limiting the size of the dictionary created in stationary
environments. We also derive as an extension the set-membership kernelized
affine projection (SM-KAP) algorithm. Finally several experiments are presented
to compare the proposed SM-NKLMS and SM-KAP algorithms to the existing methods.
Multilinear Grammar: Ranks and Interpretations
Authors: Dafydd Gibbon, Sascha Griffiths
Date: 2016-09-18 00:00:00

Multilinear Grammar provides a framework for integrating the many different
syntagmatic structures of language into a coherent semiotically based Rank
Interpretation Architecture, with default linear grammars at each rank. The
architecture defines a Sui Generis Condition on ranks, from discourse through
utterance and phrasal structures to the word, with its sub-ranks of morphology
and phonology. Each rank has unique structures and its own semantic-pragmatic
and prosodic-phonetic interpretation models. Default computational models for
each rank are proposed, based on a Procedural Plausibility Condition:
incremental processing in linear time with finite working memory. We suggest
that the Rank Interpretation Architecture and its multilinear properties
provide systematic design features of human languages, contrasting with
unordered lists of key properties or single structural properties at one rank,
such as recursion, which have previously been been put forward as language
design features. The framework provides a realistic background for the gradual
development of complexity in the phylogeny and ontogeny of language, and
clarifies a range of challenges for the evaluation of realistic linguistic
theories and applications. The empirical objective of the paper is to
demonstrate unique multilinear properties at each rank and thereby motivate the
Multilinear Grammar and Rank Interpretation Architecture framework as a
coherent approach to capturing the complexity of human languages in the
simplest possible way.
One-Shot Concept Learning by Simulating Evolutionary Instinct Development
Authors: Abrar Ahmed, Anish Bikmal
Date: 2017-08-27 00:00:00
Tasks: Object, Object Recognition

Object recognition has become a crucial part of machine learning and computer
vision recently. The current approach to object recognition involves Deep
Learning and uses Convolutional Neural Networks to learn the pixel patterns of
the objects implicitly through backpropagation. However, CNNs require thousands
of examples in order to generalize successfully and often require heavy
computing resources for training. This is considered rather sluggish when
compared to the human ability to generalize and learn new categories given just
a single example. Additionally, CNNs make it difficult to explicitly
programmatically modify or intuitively interpret their learned representations.
  We propose a computational model that can successfully learn an object
category from as few as one example and allows its learning style to be
tailored explicitly to a scenario. Our model decomposes each image into two
attributes: shape and color distribution. We then use a Bayesian criterion to
probabilistically determine the likelihood of each category. The model takes
each factor into account based on importance and calculates the conditional
probability of the object belonging to each learned category. Our model is not
only applicable to visual scenarios, but can also be implemented in a broader
and more practical scope of situations such as Natural Language Processing as
well as other places where it is possible to retrieve and construct individual
attributes. Because the only condition our model presents is the ability to
retrieve and construct individual attributes such as shape and color, it can be
applied to essentially any class of visual objects.
Methods for applying the Neural Engineering Framework to neuromorphic hardware
Authors: Aaron R. Voelker, Chris Eliasmith
Date: 2017-08-27 00:00:00

We review our current software tools and theoretical methods for applying the
Neural Engineering Framework to state-of-the-art neuromorphic hardware. These
methods can be used to implement linear and nonlinear dynamical systems that
exploit axonal transmission time-delays, and to fully account for nonideal
mixed-analog-digital synapses that exhibit higher-order dynamics with
heterogeneous time-constants. This summarizes earlier versions of these methods
that have been discussed in a more biological context (Voelker & Eliasmith,
2017) or regarding a specific neuromorphic architecture (Voelker et al., 2017).
A Machine Learning Approach For Identifying Patients with Mild Traumatic Brain Injury Using Diffusion MRI Modeling
Authors: Shervin Minaee, Yao Wang, Sohae Chung, Xiuyuan Wang, Els Fieremans, Steven Flanagan, Joseph Rath, Yvonne W. Lui
Date: 2017-08-27 00:00:00
Tasks: BIG-bench Machine Learning, Diffusion  MRI

While diffusion MRI has been extremely promising in the study of MTBI,
identifying patients with recent MTBI remains a challenge. The literature is
mixed with regard to localizing injury in these patients, however, gray matter
such as the thalamus and white matter including the corpus callosum and frontal
deep white matter have been repeatedly implicated as areas at high risk for
injury. The purpose of this study is to develop a machine learning framework to
classify MTBI patients and controls using features derived from multi-shell
diffusion MRI in the thalamus, frontal white matter and corpus callosum.
Seeded Laplaican: An Eigenfunction Solution for Scribble Based Interactive Image Segmentation
Authors: Ahmed Taha, Marwan Torki
Date: 2017-02-03 00:00:00
Tasks: Image Segmentation, Interactive Segmentation, Segmentation, Semantic Segmentation

In this paper, we cast the scribble-based interactive image segmentation as a
semi-supervised learning problem. Our novel approach alleviates the need to
solve an expensive generalized eigenvector problem by approximating the
eigenvectors using efficiently computed eigenfunctions. The smoothness operator
defined on feature densities at the limit n tends to infinity recovers the
exact eigenvectors of the graph Laplacian, where n is the number of nodes in
the graph. To further reduce the computational complexity without scarifying
our accuracy, we select pivots pixels from user annotations. In our
experiments, we evaluate our approach using both human scribble and "robot
user" annotations to guide the foreground/background segmentation. We developed
a new unbiased collection of five annotated images datasets to standardize the
evaluation procedure for any scribble-based segmentation method. We
experimented with several variations, including different feature vectors,
pivot count and the number of eigenvectors. Experiments are carried out on
datasets that contain a wide variety of natural images. We achieve better
qualitative and quantitative results compared to state-of-the-art interactive
segmentation algorithms.
Impact of Feature Selection on Micro-Text Classification
Authors: Ankit Vadehra, Maura R. Grossman, Gordon V. Cormack
Date: 2017-08-27 00:00:00
Tasks: Classification, Clustering, feature selection, General Classification, Lemmatization, Multi-class Classification, POS, Recommendation Systems, Sentiment Analysis, Spam detection, text-classification, Text Classification

Social media datasets, especially Twitter tweets, are popular in the field of
text classification. Tweets are a valuable source of micro-text (sometimes
referred to as "micro-blogs"), and have been studied in domains such as
sentiment analysis, recommendation systems, spam detection, clustering, among
others. Tweets often include keywords referred to as "Hashtags" that can be
used as labels for the tweet. Using tweets encompassing 50 labels, we studied
the impact of word versus character-level feature selection and extraction on
different learners to solve a multi-class classification task. We show that
feature extraction of simple character-level groups performs better than simple
word groups and pre-processing methods like normalizing using Porter's Stemming
and Part-of-Speech ("POS")-Lemmatization.
Part-to-whole Registration of Histology and MRI using Shape Elements
Authors: Jonas Pichat, Juan Eugenio Iglesias, Sotiris Nousias, Tarek Yousry, Sebastien Ourselin, Marc Modat
Date: 2017-08-27 00:00:00
Tasks: Image Registration

Image registration between histology and magnetic resonance imaging (MRI) is
a challenging task due to differences in structural content and contrast. Too
thick and wide specimens cannot be processed all at once and must be cut into
smaller pieces. This dramatically increases the complexity of the problem,
since each piece should be individually and manually pre-aligned. To the best
of our knowledge, no automatic method can reliably locate such piece of tissue
within its respective whole in the MRI slice, and align it without any prior
information. We propose here a novel automatic approach to the joint problem of
multimodal registration between histology and MRI, when only a fraction of
tissue is available from histology. The approach relies on the representation
of images using their level lines so as to reach contrast invariance. Shape
elements obtained via the extraction of bitangents are encoded in a
projective-invariant manner, which permits the identification of common pieces
of curves between two images. We evaluated the approach on human brain
histology and compared resulting alignments against manually annotated ground
truths. Considering the complexity of the brain folding patterns, preliminary
results are promising and suggest the use of characteristic and meaningful
shape elements for improved robustness and efficiency.
Improved Descriptors for Patch Matching and Reconstruction
Authors: Rahul Mitra, Jiakai Zhang, Sanath Narayan, Shuaib Ahmed, Sharat Chandran, Arjun Jain
Date: 2017-01-24 00:00:00
Tasks: 3D Reconstruction, Patch Matching

We propose a convolutional neural network (ConvNet) based approach for
learning local image descriptors which can be used for significantly improved
patch matching and 3D reconstructions. A multi-resolution ConvNet is used for
learning keypoint descriptors. We also propose a new dataset consisting of an
order of magnitude more number of scenes, images, and positive and negative
correspondences compared to the currently available Multi-View Stereo (MVS)
[18] dataset. The new dataset also has better coverage of the overall
viewpoint, scale, and lighting changes in comparison to the MVS dataset. We
evaluate our approach on publicly available datasets, such as Oxford Affine
Covariant Regions Dataset (ACRD) [12], MVS [18], Synthetic [6] and Strecha [15]
datasets to quantify the image descriptor performance. Scenes from the Oxford
ACRD, MVS and Synthetic datasets are used for evaluating the patch matching
performance of the learnt descriptors while the Strecha dataset is used to
evaluate the 3D reconstruction task. Experiments show that the proposed
descriptor outperforms the current state-of-the-art descriptors in both the
evaluation tasks.
Learning MSO-definable hypotheses on string
Authors: Martin Grohe, Christof Löding, Martin Ritzert
Date: 2017-08-27 00:00:00
Tasks: General Classification

We study the classification problems over string data for hypotheses
specified by formulas of monadic second-order logic MSO. The goal is to design
learning algorithms that run in time polynomial in the size of the training
set, independently of or at least sublinear in the size of the whole data set.
We prove negative as well as positive results. If the data set is an
unprocessed string to which our algorithms have local access, then learning in
sublinear time is impossible even for hypotheses definable in a small fragment
of first-order logic. If we allow for a linear time pre-processing of the
string data to build an index data structure, then learning of MSO-definable
hypotheses is possible in time polynomial in the size of the training set,
independently of the size of the whole data set.
Local Gaussian Processes for Efficient Fine-Grained Traffic Speed Prediction
Authors: Truc Viet Le, Richard J. Oentaryo, Siyuan Liu, Hoong Chuin Lau
Date: 2017-08-27 00:00:00
Tasks: Gaussian Processes

Traffic speed is a key indicator for the efficiency of an urban
transportation system. Accurate modeling of the spatiotemporally varying
traffic speed thus plays a crucial role in urban planning and development. This
paper addresses the problem of efficient fine-grained traffic speed prediction
using big traffic data obtained from static sensors. Gaussian processes (GPs)
have been previously used to model various traffic phenomena, including flow
and speed. However, GPs do not scale with big traffic data due to their cubic
time complexity. In this work, we address their efficiency issues by proposing
local GPs to learn from and make predictions for correlated subsets of data.
The main idea is to quickly group speed variables in both spatial and temporal
dimensions into a finite number of clusters, so that future and unobserved
traffic speed queries can be heuristically mapped to one of such clusters. A
local GP corresponding to that cluster can then be trained on the fly to make
predictions in real-time. We call this method localization. We use non-negative
matrix factorization for localization and propose simple heuristics for cluster
mapping. We additionally leverage on the expressiveness of GP kernel functions
to model road network topology and incorporate side information. Extensive
experiments using real-world traffic data collected in the two U.S. cities of
Pittsburgh and Washington, D.C., show that our proposed local GPs significantly
improve both runtime performances and prediction accuracies compared to the
baseline global and local GPs.
Anomaly Detection in Wireless Sensor Networks
Authors: Pelumi Oluwasanya
Date: 2017-08-27 00:00:00
Tasks: Anomaly Detection

Wireless sensor networks usually comprise a large number of sensors
monitoring changes in variables. These changes in variables represent changes
in physical quantities. The changes can occur for various reasons; these
reasons are highlighted in this work. Outliers are unusual measurements.
Outliers are important; they are information-bearing occurrences. This work
seeks to identify them based on an approach presented in [1]. A critical review
of most previous works in this area has been presented in [2], and few more are
considered here just to set the stage. The main work can be described as this;
given a set of measurements from sensors that represent a normal situation, [1]
proceeds by first estimating the probability density function (pdf) of the set
using a data-split approach, then estimate the entropy of the set using the
arithmetic mean as an approximation for the expectation. The increase in
entropy that occurs when strange data is recorded is used to detect unusual
measurements in the test set depending on the desired confidence interval or
false alarm rate. The results presented in [1] have been confirmed for
different test signals such as the Gaussian, Beta, in one dimension and beta in
two dimensions, and a beta and uniform mixture distribution in two dimensions.
Finally, the method was confirmed on real data and the results are presented.
The major drawbacks of [1] were identified, and a method that seeks to mitigate
this using the Bhattacharyya distance is presented. This method detects more
subtle anomalies, especially the type that would pass as normal in [1].
Finally, recommendations for future research are presented: the subject of
interpretability, especially for subtle measurements, being the most elusive as
of today.