pwc-archive/papers-with-abstracts huggingface dataset:
Applying Data Augmentation to Handwritten Arabic Numeral Recognition Using Deep Learning Neural Networks
Authors: Akm Ashiquzzaman, Abdul Kawsar Tushar, Md Ashiqur Rahman
Date: 2017-08-20 00:00:00
Tasks: Data Augmentation

Handwritten character recognition has been the center of research and a benchmark problem in the sector of pattern recognition and artificial intelligence, and it continues to be a challenging research topic. Due to its enormous application many works have been done in this field focusing on different languages. Arabic, being a diversified language has a huge scope of research with potential challenges. A convolutional neural network model for recognizing handwritten numerals in Arabic language is proposed in this paper, where the dataset is subject to various augmentation in order to add robustness needed for deep learning approach. The proposed method is empowered by the presence of dropout regularization to do away with the problem of data overfitting. Moreover, suitable change is introduced in activation function to overcome the problem of vanishing gradient. With these modifications, the proposed system achieves an accuracy of 99.4\% which performs better than every previous work on the dataset.
ANSAC: Adaptive Non-minimal Sample and Consensus
Authors: Victor Fragoso, Chris Sweeney, Pradeep Sen, Matthew Turk
Date: 2017-09-27 00:00:00

While RANSAC-based methods are robust to incorrect image correspondences
(outliers), their hypothesis generators are not robust to correct image
correspondences (inliers) with positional error (noise). This slows down their
convergence because hypotheses drawn from a minimal set of noisy inliers can
deviate significantly from the optimal model. This work addresses this problem
by introducing ANSAC, a RANSAC-based estimator that accounts for noise by
adaptively using more than the minimal number of correspondences required to
generate a hypothesis. ANSAC estimates the inlier ratio (the fraction of
correct correspondences) of several ranked subsets of candidate correspondences
and generates hypotheses from them. Its hypothesis-generation mechanism
prioritizes the use of subsets with high inlier ratio to generate high-quality
hypotheses. ANSAC uses an early termination criterion that keeps track of the
inlier ratio history and terminates when it has not changed significantly for a
period of time. The experiments show that ANSAC finds good homography and
fundamental matrix estimates in a few iterations, consistently outperforming
state-of-the-art methods.
Introducing machine learning for power system operation support
Authors: Benjamin Donnot, Isabelle Guyon, Marc Schoenauer, Patrick Panciatici, Antoine Marot
Date: 2017-09-27 00:00:00
Tasks: BIG-bench Machine Learning, RTE

We address the problem of assisting human dispatchers in operating power
grids in today's changing context using machine learning, with theaim of
increasing security and reducing costs. Power networks are highly regulated
systems, which at all times must meet varying demands of electricity with a
complex production system, including conventional power plants, less
predictable renewable energies (such as wind or solar power), and the
possibility of buying/selling electricity on the international market with more
and more actors involved at a Europeanscale. This problem is becoming ever more
challenging in an aging network infrastructure. One of the primary goals of
dispatchers is to protect equipment (e.g. avoid that transmission lines
overheat) with few degrees of freedom: we are considering in this paper solely
modifications in network topology, i.e. re-configuring the way in which lines,
transformers, productions and loads are connected in sub-stations. Using years
of historical data collected by the French Transmission Service Operator (TSO)
"R\'eseau de Transport d'Electricit\'e" (RTE), we develop novel machine
learning techniques (drawing on "deep learning") to mimic human decisions to
devise "remedial actions" to prevent any line to violate power flow limits
(so-called "thermal limits"). The proposed technique is hybrid. It does not
rely purely on machine learning: every action will be tested with actual
simulators before being proposed to the dispatchers or implemented on the grid.
Bayesian Pool-based Active Learning With Abstention Feedbacks
Authors: Cuong V. Nguyen, Lam Si Tung Ho, Huan Xu, Vu Dinh, Binh Nguyen
Date: 2017-05-23 00:00:00
Tasks: Active Learning, General Classification

We study pool-based active learning with abstention feedbacks, where a labeler can abstain from labeling a queried example with some unknown abstention rate. This is an important problem with many useful applications. We take a Bayesian approach to the problem and develop two new greedy algorithms that learn both the classification problem and the unknown abstention rate at the same time. These are achieved by simply incorporating the estimated abstention rate into the greedy criteria. We prove that both of our algorithms have near-optimality guarantees: they respectively achieve a ${(1-\frac{1}{e})}$ constant factor approximation of the optimal expected or worst-case value of a useful utility function. Our experiments show the algorithms perform well in various practical scenarios.
Modeling the Resource Requirements of Convolutional Neural Networks on Mobile Devices
Authors: Zongqing Lu, Swati Rallapalli, Kevin Chan, Thomas La Porta
Date: 2017-09-27 00:00:00
Tasks: Self-Driving Cars

Convolutional Neural Networks (CNNs) have revolutionized the research in
computer vision, due to their ability to capture complex patterns, resulting in
high inference accuracies. However, the increasingly complex nature of these
neural networks means that they are particularly suited for server computers
with powerful GPUs. We envision that deep learning applications will be
eventually and widely deployed on mobile devices, e.g., smartphones,
self-driving cars, and drones. Therefore, in this paper, we aim to understand
the resource requirements (time, memory) of CNNs on mobile devices. First, by
deploying several popular CNNs on mobile CPUs and GPUs, we measure and analyze
the performance and resource usage for every layer of the CNNs. Our findings
point out the potential ways of optimizing the performance on mobile devices.
Second, we model the resource requirements of the different CNN computations.
Finally, based on the measurement, pro ling, and modeling, we build and
evaluate our modeling tool, Augur, which takes a CNN configuration (descriptor)
as the input and estimates the compute time and resource usage of the CNN, to
give insights about whether and how e ciently a CNN can be run on a given
mobile platform. In doing so Augur tackles several challenges: (i) how to
overcome pro ling and measurement overhead; (ii) how to capture the variance in
different mobile platforms with different processors, memory, and cache sizes;
and (iii) how to account for the variance in the number, type and size of
layers of the different CNN configurations.
Replicability Analysis for Natural Language Processing: Testing Significance with Multiple Datasets
Authors: Rotem Dror, Gili Baumer, Marina Bogomolov, Roi Reichart
Date: 2017-09-27 00:00:00
Proceeding: TACL 2017 1
Tasks: Dependency Parsing, General Classification, POS, POS Tagging, Sentiment Analysis, Sentiment Classification, Word Similarity

With the ever-growing amounts of textual data from a large variety of
languages, domains, and genres, it has become standard to evaluate NLP
algorithms on multiple datasets in order to ensure consistent performance
across heterogeneous setups. However, such multiple comparisons pose
significant challenges to traditional statistical analysis methods in NLP and
can lead to erroneous conclusions. In this paper, we propose a Replicability
Analysis framework for a statistically sound analysis of multiple comparisons
between algorithms for NLP tasks. We discuss the theoretical advantages of this
framework over the current, statistically unjustified, practice in the NLP
literature, and demonstrate its empirical value across four applications:
multi-domain dependency parsing, multilingual POS tagging, cross-domain
sentiment classification and word similarity prediction.
A Literature Based Approach to Define the Scope of Biomedical Ontologies: A Case Study on a Rehabilitation Therapy Ontology
Authors: Mohammad K. Halawani, Rob Forsyth, Phillip Lord
Date: 2017-09-27 00:00:00

In this article, we investigate our early attempts at building an ontology
describing rehabilitation therapies following brain injury. These therapies are
wide-ranging, involving interventions of many different kinds. As a result,
these therapies are hard to describe. As well as restricting actual practice,
this is also a major impediment to evidence-based medicine as it is hard to
meaningfully compare two treatment plans.
  Ontology development requires significant effort from both ontologists and
domain experts. Knowledge elicited from domain experts forms the scope of the
ontology. The process of knowledge elicitation is expensive, consumes experts'
time and might have biases depending on the selection of the experts. Various
methodologies and techniques exist for enabling this knowledge elicitation,
including community groups and open development practices. A related problem is
that of defining scope. By defining the scope, we can decide whether a concept
(i.e. term) should be represented in the ontology. This is the opposite of
knowledge elicitation, in the sense that it defines what should not be in the
ontology. This can be addressed by pre-defining a set of competency questions.
  These approaches are, however, expensive and time-consuming. Here, we
describe our work toward an alternative approach, bootstrapping the ontology
from an initially small corpus of literature that will define the scope of the
ontology, expanding this to a set covering the domain, then using information
extraction to define an initial terminology to provide the basis and the
competencies for the ontology. Here, we discuss four approaches to building a
suitable corpus that is both sufficiently covering and precise.
Prosodic Features from Large Corpora of Child-Directed Speech as Predictors of the Age of Acquisition of Words
Authors: Lea Frermann, Michael C. Frank
Date: 2017-09-27 00:00:00

The impressive ability of children to acquire language is a widely studied
phenomenon, and the factors influencing the pace and patterns of word learning
remains a subject of active research. Although many models predicting the age
of acquisition of words have been proposed, little emphasis has been directed
to the raw input children achieve. In this work we present a comparatively
large-scale multi-modal corpus of prosody-text aligned child directed speech.
Our corpus contains automatically extracted word-level prosodic features, and
we investigate the utility of this information as predictors of age of
acquisition. We show that prosody features boost predictive power in a
regularized regression, and demonstrate their utility in the context of a
multi-modal factorized language models trained and tested on child-directed
speech.
Fine-Grain Annotation of Cricket Videos
Authors: Rahul Anand Sharma, Pramod Sankar K, CV Jawahar
Date: 2015-11-24 00:00:00
Tasks: Action Recognition, Retrieval, Temporal Action Localization, Video Understanding

The recognition of human activities is one of the key problems in video
understanding. Action recognition is challenging even for specific categories
of videos, such as sports, that contain only a small set of actions.
Interestingly, sports videos are accompanied by detailed commentaries available
online, which could be used to perform action annotation in a weakly-supervised
setting. For the specific case of Cricket videos, we address the challenge of
temporal segmentation and annotation of ctions with semantic descriptions. Our
solution consists of two stages. In the first stage, the video is segmented
into "scenes", by utilizing the scene category information extracted from
text-commentary. The second stage consists of classifying video-shots as well
as the phrases in the textual description into various categories. The relevant
phrases are then suitably mapped to the video-shots. The novel aspect of this
work is the fine temporal scale at which semantic information is assigned to
the video. As a result of our approach, we enable retrieval of specific actions
that last only a few seconds, from several hours of video. This solution yields
a large number of labeled exemplars, with no manual effort, that could be used
by machine learning algorithms to learn complex actions.
Scene learning, recognition and similarity detection in a fuzzy ontology via human examples
Authors: Luca Buoncompagni, Fulvio Mastrogiovanni, Alessandro Saffiotti
Date: 2017-09-27 00:00:00

This paper introduces a Fuzzy Logic framework for scene learning, recognition
and similarity detection, where scenes are taught via human examples. The
framework allows a robot to: (i) deal with the intrinsic vagueness associated
with determining spatial relations among objects; (ii) infer similarities and
dissimilarities in a set of scenes, and represent them in a hierarchical
structure represented in a Fuzzy ontology. In this paper, we briefly formalize
our approach and we provide a few use cases by way of illustration.
Nevertheless, we discuss how the framework can be used in real-world scenarios.
FoodNet: Recognizing Foods Using Ensemble of Deep Networks
Authors: Paritosh Pandey, Akella Deepthi, Bappaditya Mandal, N. B. Puhan
Date: 2017-09-27 00:00:00
Tasks: Food Recognition, General Classification

In this work we propose a methodology for an automatic food classification
system which recognizes the contents of the meal from the images of the food.
We developed a multi-layered deep convolutional neural network (CNN)
architecture that takes advantages of the features from other deep networks and
improves the efficiency. Numerous classical handcrafted features and approaches
are explored, among which CNNs are chosen as the best performing features.
Networks are trained and fine-tuned using preprocessed images and the filter
outputs are fused to achieve higher accuracy. Experimental results on the
largest real-world food recognition database ETH Food-101 and newly contributed
Indian food image database demonstrate the effectiveness of the proposed
methodology as compared to many other benchmark deep learned CNN frameworks.
Using Titles vs. Full-text as Source for Automated Semantic Document Annotation
Authors: Lukas Galke, Florian Mai, Alan Schelten, Dennis Brunsch, Ansgar Scherp
Date: 2017-05-15 00:00:00
Tasks: Articles, Classification, Document Classification, General Classification, Knowledge Graphs, Learning-To-Rank, text-classification, Text Classification

A significant part of the largest Knowledge Graph today, the Linked Open Data
cloud, consists of metadata about documents such as publications, news reports,
and other media articles. While the widespread access to the document metadata
is a tremendous advancement, it is yet not so easy to assign semantic
annotations and organize the documents along semantic concepts. Providing
semantic annotations like concepts in SKOS thesauri is a classical research
topic, but typically it is conducted on the full-text of the documents. For the
first time, we offer a systematic comparison of classification approaches to
investigate how far semantic annotations can be conducted using just the
metadata of the documents such as titles published as labels on the Linked Open
Data cloud. We compare the classifications obtained from analyzing the
documents' titles with semantic annotations obtained from analyzing the
full-text. Apart from the prominent text classification baselines kNN and SVM,
we also compare recent techniques of Learning to Rank and neural networks and
revisit the traditional methods logistic regression, Rocchio, and Naive Bayes.
The results show that across three of our four datasets, the performance of the
classifications using only titles reaches over 90% of the quality compared to
the classification performance when using the full-text. Thus, conducting
document classification by just using the titles is a reasonable approach for
automated semantic annotation and opens up new possibilities for enriching
Knowledge Graphs.
Leveraging Weakly Annotated Data for Fashion Image Retrieval and Label Prediction
Authors: Charles Corbière, Hedi Ben-Younes, Alexandre Ramé, Charles Ollion
Date: 2017-09-27 00:00:00
Tasks: General Classification, Image Retrieval, Retrieval, Weakly-supervised Learning

In this paper, we present a method to learn a visual representation adapted
for e-commerce products. Based on weakly supervised learning, our model learns
from noisy datasets crawled on e-commerce website catalogs and does not require
any manual labeling. We show that our representation can be used for downward
classification tasks over clothing categories with different levels of
granularity. We also demonstrate that the learnt representation is suitable for
image retrieval. We achieve nearly state-of-art results on the DeepFashion
In-Shop Clothes Retrieval and Categories Attributes Prediction tasks, without
using the provided training set.
Abnormality Detection and Localization in Chest X-Rays using Deep Convolutional Neural Networks
Authors: Mohammad Tariqul Islam, Md Abdul Aowal, Ahmed Tahseen Minhaz, Khalid Ashraf
Date: 2017-05-27 00:00:00
Tasks: Anomaly Detection, General Classification

Chest X-Rays (CXRs) are widely used for diagnosing abnormalities in the heart
and lung area. Automatically detecting these abnormalities with high accuracy
could greatly enhance real world diagnosis processes. Lack of standard publicly
available dataset and benchmark studies, however, makes it difficult to compare
various detection methods. In order to overcome these difficulties, we have
used a publicly available Indiana CXR, JSRT and Shenzhen dataset and studied
the performance of known deep convolutional network (DCN) architectures on
different abnormalities. We find that the same DCN architecture doesn't perform
well across all abnormalities. Shallow features or earlier layers consistently
provide higher detection accuracy compared to deep features. We have also found
ensemble models to improve classification significantly compared to single
model. Combining these insight, we report the highest accuracy on chest X-Ray
abnormality detection on these datasets. We find that for cardiomegaly
detection, the deep learning method improves the accuracy by a staggering 17
percentage point compared to rule based methods. We applied the techniques to
the problem of tuberculosis detection on a different dataset and achieved the
highest accuracy. Our localization experiments using these trained classifiers
show that for spatially spread out abnormalities like cardiomegaly and
pulmonary edema, the network can localize the abnormalities successfully most
of the time. One remarkable result of the cardiomegaly localization is that the
heart and its surrounding region is most responsible for cardiomegaly
detection, in contrast to the rule based models where the ratio of heart and
lung area is used as the measure. We believe that through deep learning based
classification and localization, we will discover many more interesting
features in medical image diagnosis that are not considered traditionally.
A Preliminary Study for Building an Arabic Corpus of Pair Questions-Texts from the Web: AQA-Webcorp
Authors: Wided Bakari, Patrice Bellot, Mahmoud Neji
Date: 2017-09-27 00:00:00
Tasks: Information Retrieval, Machine Translation, Retrieval, Translation

With the development of electronic media and the heterogeneity of Arabic data
on the Web, the idea of building a clean corpus for certain applications of
natural language processing, including machine translation, information
retrieval, question answer, become more and more pressing. In this manuscript,
we seek to create and develop our own corpus of pair's questions-texts. This
constitution then will provide a better base for our experimentation step.
Thus, we try to model this constitution by a method for Arabic insofar as it
recovers texts from the web that could prove to be answers to our factual
questions. To do this, we had to develop a java script that can extract from a
given query a list of html pages. Then clean these pages to the extent of
having a data base of texts and a corpus of pair's question-texts. In addition,
we give preliminary results of our proposal method. Some investigations for the
construction of Arabic corpus are also presented in this document.
Slim-DP: A Light Communication Data Parallelism for DNN
Authors: Shizhao Sun, Wei Chen, Jiang Bian, Xiaoguang Liu, Tie-Yan Liu
Date: 2017-09-27 00:00:00

Data parallelism has emerged as a necessary technique to accelerate the
training of deep neural networks (DNN). In a typical data parallelism approach,
the local workers push the latest updates of all the parameters to the
parameter server and pull all merged parameters back periodically. However,
with the increasing size of DNN models and the large number of workers in
practice, this typical data parallelism cannot achieve satisfactory training
acceleration, since it usually suffers from the heavy communication cost due to
transferring huge amount of information between workers and the parameter
server. In-depth understanding on DNN has revealed that it is usually highly
redundant, that deleting a considerable proportion of the parameters will not
significantly decline the model performance. This redundancy property exposes a
great opportunity to reduce the communication cost by only transferring the
information of those significant parameters during the parallel training.
However, if we only transfer information of temporally significant parameters
of the latest snapshot, we may miss the parameters that are insignificant now
but have potential to become significant as the training process goes on. To
this end, we design an Explore-Exploit framework to dynamically choose the
subset to be communicated, which is comprised of the significant parameters in
the latest snapshot together with a random explored set of other parameters. We
propose to measure the significance of the parameter by the combination of its
magnitude and gradient. Our experimental results demonstrate that our proposed
Slim-DP can achieve better training acceleration than standard data parallelism
and its communication-efficient version by saving communication time without
loss of accuracy.
Human Detection for Night Surveillance using Adaptive Background Subtracted Image
Authors: Yash Khandhediya, Karishma Sav, Vandit Gajjar
Date: 2017-09-27 00:00:00
Tasks: Human Detection

Surveillance based on Computer Vision has become a major necessity in current
era. Most of the surveillance systems operate on visible light imaging, but
performance based on visible light imaging is limited due to some factors like
variation in light intensity during the daytime. The matter of concern lies in
the need for processing images in low light, such as in the need of nighttime
surveillance. In this paper, we have proposed a novel approach for human
detection using FLIR(Forward Looking Infrared) camera. As the principle
involves sensing based on thermal radiation in the Near IR Region, it is
possible to detect Humans from an image captured using a FLIR camera even in
low light. The proposed method for human detection involves processing of
Thermal images by using HOG (Histogram of Oriented Gradients) feature
extraction technique along with some enhancements. The principle of the
proposed technique lies in an adaptive background subtraction algorithm, which
works in association with the HOG technique. By means of this method, we are
able to reduce execution time, precision and some other parameters, which
result in improvement of overall accuracy of the human detection system.