pwc-archive/papers-with-abstracts huggingface dataset:
Tackling interoperability issues within UIMA work flows
Authors: Hern, Nicolas ez
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Management, POS

One of the major issues dealing with any workflow management frameworks is the components interoperability. In this paper, we are concerned with the Apache UIMA framework. We address the problem by considering separately the development of new components and the integration of existing tools. For the former objective, we propose an API to generically handle TS objects by their name using reflexivity in order to make the components TS-independent. In the latter case, we distinguish the case of aggregating heterogeneous TS-dependent UIMA components from the case of integrating non UIMA-native third party tools. We propose a mapper component to aggregate TS-dependent UIMA components. And we propose a component to wrap command lines third party tools and a set of components to connect various markup languages with the UIMA data structure. Finally, we present two situations where these solutions were effectively used: Training a POS tagger system from a treebank, and embedding an external POS tagger in a workflow. Our approch aims at providing quick development solutions.
Ubiquitous Usage of a Broad Coverage French Corpus: Processing the Est Republicain corpus
Authors: Djam{\'e} Seddah, C, Marie ito, Benoit Crabb{\'e}, Enrique Henestroza Anguiano
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Articles, Dependency Parsing, Lemmatization, Part-Of-Speech Tagging

In this paper, we introduce a set of resources that we have derived from the EST R{\'E}PUBLICAIN CORPUS, a large, freely-available collection of regional newspaper articles in French, totaling 150 million words. Our resources are the result of a full NLP treatment of the EST R{\'E}PUBLICAIN CORPUS: handling of multi-word expressions, lemmatization, part-of-speech tagging, and syntactic parsing. Processing of the corpus is carried out using statistical machine-learning approaches - joint model of data driven lemmatization and part- of-speech tagging, PCFG-LA and dependency based models for parsing - that have been shown to achieve state-of-the-art performance when evaluated on the French Treebank. Our derived resources are made freely available, and released according to the original Creative Common license for the EST R{\'E}PUBLICAIN CORPUS. We additionally provide an overview of the use of these resources in various applications, in particular the use of generated word clusters from the corpus to alleviate lexical data sparseness for statistical parsing.
Wordnet extension made simple: A multilingual lexicon-based approach using wiki resources
Authors: Val{\'e}rie Hanoka, Beno{\^\i}t Sagot
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Translation, Word Sense Disambiguation

In this paper, we propose a simple methodology for building or extending wordnets using easily extractible lexical knowledge from Wiktionary and Wikipedia. This method relies on a large multilingual translation/synonym graph in many languages as well as synset-aligned wordnets. It guesses frequent and polysemous literals that are difficult to find using other methods by looking at back-translations in the graph, showing that the use of a heavily multilingual lexicon can be a way to mitigate the lack of wide coverage bilingual lexicon for wordnet creation or extension. We evaluate our approach on French by applying it for extending WOLF, a freely available French wordnet.
Development of Text and Speech database for Hindi and Indian English specific to Mobile Communication environment
Authors: Shyam Agrawal, Shweta Sinha, Pooja Singh, Jesper Olson
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Language Identification, Speech Recognition

Abstract This paper describes the method and experiences of text and speech data collection in mobile communication in Indian English Hindi. The primary data collection is done in the form of large number of messages as part of Personal communication among natives of Hindi language and Indian speakers of English. To gather the versatility of mobile communication database among Hindi and English, 12 domains were identified for collection of text corpus from speaking population belonging to deferent age groups, sex and dialects. The text obtained in raw form based on slangs and unconventional grammar were cleaned using on language grammar rules and then tagged and expanded to explain context specific meaning of the words. Texts of 1163 participants from Hindi speaking regions and 1405 English users were taken for creating 13 prompt sheets; containing 630 phonetically rich sentences created using a special software. Each prompt sheet was recorded by at least 7 users simultaneously in three channels and recorded by a total of 100 speakers and annotated. The work is a step forward in the direction of development of standards for mobile text and speech data collection for Indian languages. Keywords - Speech data base, Text analysis, mobile communication, Hindi and Indian English Speech, multi-lingual speech processing.
Deep Morphological Simplification Network (MS-Net) for Guided Registration of Brain Magnetic Resonance Images
Authors: Dongming Wei, Zhengwang Wu, Gang Li, Xiaohuan Cao, Dinggang Shen, Qian Wang
Date: 2019-02-06 00:00:00
Tasks: Image Registration

Objective: Deformable brain MR image registration is challenging due to large
inter-subject anatomical variation. For example, the highly complex cortical
folding pattern makes it hard to accurately align corresponding cortical
structures of individual images. In this paper, we propose a novel deep
learning way to simplify the difficult registration problem of brain MR images.
Methods: We train a morphological simplification network (MS-Net), which can
generate a "simple" image with less anatomical details based on the "complex"
input. With MS-Net, the complexity of the fixed image or the moving image under
registration can be reduced gradually, thus building an individual
(simplification) trajectory represented by MS-Net outputs. Since the generated
images at the ends of the two trajectories (of the fixed and moving images) are
so simple and very similar in appearance, they are easy to register. Thus, the
two trajectories can act as a bridge to link the fixed and the moving images,
and guide their registration. Results: Our experiments show that the proposed
method can achieve highly accurate registration performance on different
datasets (i.e., NIREP, LPBA, IBSR, CUMC, and MGH). Moreover, the method can be
also easily transferred across diverse image datasets and obtain superior
accuracy on surface alignment. Conclusion and Significance: We propose MS-Net
as a powerful and flexible tool to simplify brain MR images and their
registration. To our knowledge, this is the first work to simplify brain MR
image registration by deep learning, instead of estimating deformation field
directly.
Semi-Supervised Learning by Label Gradient Alignment
Authors: Jacob Jackson, John Schulman
Date: 2019-02-06 00:00:00
Tasks: General Classification

We present label gradient alignment, a novel algorithm for semi-supervised
learning which imputes labels for the unlabeled data and trains on the imputed
labels. We define a semantically meaningful distance metric on the input space
by mapping a point (x, y) to the gradient of the model at (x, y). We then
formulate an optimization problem whose objective is to minimize the distance
between the labeled and the unlabeled data in this space, and we solve it by
gradient descent on the imputed labels. We evaluate label gradient alignment
using the standardized architecture introduced by Oliver et al. (2018) and
demonstrate state-of-the-art accuracy in semi-supervised CIFAR-10
classification.
Extending a model for ontology-based Arabic-English machine translation
Authors: Neama Abdulaziz Dahan, Fadl Mutaher Ba-Alwi
Date: 2019-02-06 00:00:00
Tasks: Articles, Machine Translation, Translation

The acceleration in telecommunication needs leads to many groups of research,
especially in communication facilitating and Machine Translation fields. While
people contact with others having different languages and cultures, they need
to have instant translations. However, the available instant translators are
still providing somewhat bad Arabic-English Translations, for instance when
translating books or articles, the meaning is not totally accurate. Therefore,
using the semantic web techniques to deal with the homographs and homonyms
semantically, the aim of this research is to extend a model for the
ontology-based Arabic-English Machine Translation, named NAN, which simulate
the human way in translation. The experimental results show that NAN
translation is approximately more similar to the Human Translation than the
other instant translators. The resulted translation will help getting the
translated texts in the target language somewhat correctly and semantically
more similar to human translations for the Non-Arabic Natives and the
Non-English natives.
Is AmI (Attacks Meet Interpretability) Robust to Adversarial Examples?
Authors: Nicholas Carlini
Date: 2019-02-06 00:00:00
Tasks: Adversarial Attack, Adversarial Defense

No.
Decentralized Multi-Agents by Imitation of a Centralized Controller
Authors: Alex Tong Lin, Mark J. Debord, Katia Estabridis, Gary Hewer, Guido Montufar, Stanley Osher
Date: 2019-02-06 00:00:00
Tasks: Imitation Learning, Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We consider a multi-agent reinforcement learning problem where each agent seeks to maximize a shared reward while interacting with other agents, and they may or may not be able to communicate. Typically the agents do not have access to other agent policies and thus each agent is situated in a non-stationary and partially-observable environment. In order to obtain multi-agents that act in a decentralized manner, we introduce a novel algorithm under the popular framework of centralized training, but decentralized execution. This training framework first obtains solutions to a multi-agent problem with a single centralized joint-space learner, which is then used to guide imitation learning for independent decentralized multi-agents. This framework has the flexibility to use any reinforcement learning algorithm to obtain the expert as well as any imitation learning algorithm to obtain the decentralized agents. This is in contrast to other multi-agent learning algorithms that, for example, can require more specific structures. We present some theoretical bounds for our method, and we show that one can obtain decentralized solutions to a multi-agent problem through imitation learning.
Decentralized Flood Forecasting Using Deep Neural Networks
Authors: Muhammed Sit, Ibrahim Demir
Date: 2019-02-06 00:00:00
Tasks: Management, Time Series, Time Series Analysis, Time Series Forecasting

Predicting flood for any location at times of extreme storms is a longstanding problem that has utmost importance in emergency management. Conventional methods that aim to predict water levels in streams use advanced hydrological models still lack of giving accurate forecasts everywhere. This study aims to explore artificial deep neural networks' performance on flood prediction. While providing models that can be used in forecasting stream stage, this paper presents a dataset that focuses on the connectivity of data points on river networks. It also shows that neural networks can be very helpful in time-series forecasting as in flood events, and support improving existing models through data assimilation.
Neural Network Attributions: A Causal Perspective
Authors: Aditya Chattopadhyay, Piyushi Manupriya, Anirban Sarkar, Vineeth N. Balasubramanian
Date: 2019-02-06 00:00:00

We propose a new attribution method for neural networks developed using first principles of causality (to the best of our knowledge, the first such). The neural network architecture is viewed as a Structural Causal Model, and a methodology to compute the causal effect of each feature on the output is presented. With reasonable assumptions on the causal structure of the input data, we propose algorithms to efficiently compute the causal effects, as well as scale the approach to data with large dimensionality. We also show how this method can be used for recurrent neural networks. We report experimental results on both simulated and real datasets showcasing the promise and usefulness of the proposed algorithm.
Deep CSI Learning for Gait Biometric Sensing and Recognition
Authors: Kalvik Jakkala, Arupjyoti Bhuya, Zhi Sun, Pu Wang, Zhuo Cheng
Date: 2019-02-06 00:00:00
Tasks: Denoising, Gait Identification, User Identification

Gait is a person's natural walking style and a complex biological process
that is unique to each person. Recently, the channel state information (CSI) of
WiFi devices have been exploited to capture human gait biometrics for user
identification. However, the performance of existing CSI-based gait
identification systems is far from satisfactory. They can only achieve limited
identification accuracy (maximum $93\%$) only for a very small group of people
(i.e., between 2 to 10). To address such challenge, an end-to-end deep CSI
learning system is developed, which exploits deep neural networks to
automatically learn the salient gait features in CSI data that are
discriminative enough to distinguish different people Firstly, the raw CSI data
are sanitized through window-based denoising, mean centering and normalization.
The sanitized data is then passed to a residual deep convolutional neural
network (DCNN), which automatically extracts the hierarchical features of
gait-signatures embedded in the CSI data. Finally, a softmax classifier
utilizes the extracted features to make the final prediction about the identity
of the user. In a typical indoor environment, a top-1 accuracy of $97.12 \pm
1.13\%$ is achieved for a dataset of 30 people.
A Guiding Principle for Causal Decision Problems
Authors: M. Gonzalez-Soto, L. E. Sucar, H. J. Escalante
Date: 2019-02-06 00:00:00
Tasks: Decision Making, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We define a Causal Decision Problem as a Decision Problem where the available
actions, the family of uncertain events and the set of outcomes are related
through the variables of a Causal Graphical Model $\mathcal{G}$. A solution
criteria based on Pearl's Do-Calculus and the Expected Utility criteria for
rational preferences is proposed. The implementation of this criteria leads to
an on-line decision making procedure that has been shown to have similar
performance to classic Reinforcement Learning algorithms while allowing for a
causal model of an environment to be learned. Thus, we aim to provide the
theoretical guarantees of the usefulness and optimality of a decision making
procedure based on causal information.
Unsupervised Polyglot Text To Speech
Authors: Eliya Nachmani, Lior Wolf
Date: 2019-02-06 00:00:00
Tasks: text-to-speech, Text to Speech

We present a TTS neural network that is able to produce speech in multiple
languages. The proposed network is able to transfer a voice, which was
presented as a sample in a source language, into one of several target
languages. Training is done without using matching or parallel data, i.e.,
without samples of the same speaker in multiple languages, making the method
much more applicable. The conversion is based on learning a polyglot network
that has multiple per-language sub-networks and adding loss terms that preserve
the speaker's identity in multiple languages. We evaluate the proposed polyglot
neural network for three languages with a total of more than 400 speakers and
demonstrate convincing conversion capabilities.
CLEAR: A Consistent Lifting, Embedding, and Alignment Rectification Algorithm for Multi-View Data Association
Authors: Kaveh Fathian, Kasra Khosoussi, Yulun Tian, Parker Lusk, Jonathan P. How
Date: 2019-02-06 00:00:00
Tasks: Clustering, Graph Clustering, Spectral Graph Clustering

Many robotics applications require alignment and fusion of observations obtained at multiple views to form a global model of the environment. Multi-way data association methods provide a mechanism to improve alignment accuracy of pairwise associations and ensure their consistency. However, existing methods that solve this computationally challenging problem are often too slow for real-time applications. Furthermore, some of the existing techniques can violate the cycle consistency principle, thus drastically reducing the fusion accuracy. This work presents the CLEAR (Consistent Lifting, Embedding, and Alignment Rectification) algorithm to address these issues. By leveraging insights from the multi-way matching and spectral graph clustering literature, CLEAR provides cycle consistent and accurate solutions in a computationally efficient manner. Numerical experiments on both synthetic and real datasets are carried out to demonstrate the scalability and superior performance of our algorithm in real-world problems. This algorithmic framework can provide significant improvement in the accuracy and efficiency of existing discrete assignment problems, which traditionally use pairwise (but potentially inconsistent) correspondences. An implementation of CLEAR is made publicly available online.
Generative Image Translation for Data Augmentation of Bone Lesion Pathology
Authors: Anant Gupta, Srivas Venkatesh, Sumit Chopra, Christian Ledig
Date: 2019-02-06 00:00:00
Tasks: Binary Classification, Data Augmentation, General Classification, Generative Adversarial Network, Lesion Detection, Transfer Learning, Translation

Insufficient training data and severe class imbalance are often limiting
factors when developing machine learning models for the classification of rare
diseases. In this work, we address the problem of classifying bone lesions from
X-ray images by increasing the small number of positive samples in the training
set. We propose a generative data augmentation approach based on a
cycle-consistent generative adversarial network that synthesizes bone lesions
on images without pathology. We pose the generative task as an image-patch
translation problem that we optimize specifically for distinct bones (humerus,
tibia, femur). In experimental results, we confirm that the described method
mitigates the class imbalance problem in the binary classification task of bone
lesion detection. We show that the augmented training sets enable the training
of superior classifiers achieving better performance on a held-out test set.
Additionally, we demonstrate the feasibility of transfer learning and apply a
generative model that was trained on one body part to another.
Bandit Multiclass Linear Classification: Efficient Algorithms for the Separable Case
Authors: Alina Beygelzimer, Dávid Pál, Balázs Szörényi, Devanathan Thiruvenkatachari, Chen-Yu Wei, Chicheng Zhang
Date: 2019-02-06 00:00:00
Tasks: Classification, General Classification

We study the problem of efficient online multiclass linear classification with bandit feedback, where all examples belong to one of $K$ classes and lie in the $d$-dimensional Euclidean space. Previous works have left open the challenge of designing efficient algorithms with finite mistake bounds when the data is linearly separable by a margin $\gamma$. In this work, we take a first step towards this problem. We consider two notions of linear separability: strong and weak. 1. Under the strong linear separability condition, we design an efficient algorithm that achieves a near-optimal mistake bound of $O\left( K/\gamma^2 \right)$. 2. Under the more challenging weak linear separability condition, we design an efficient algorithm with a mistake bound of $\min (2^{\widetilde{O}(K \log^2 (1/\gamma))}, 2^{\widetilde{O}(\sqrt{1/\gamma} \log K)})$. Our algorithm is based on kernel Perceptron, which is inspired by the work of (Klivans and Servedio, 2008) on improperly learning intersection of halfspaces.
Equal Opportunity in Online Classification with Partial Feedback
Authors: Yahav Bechavod, Katrina Ligett, Aaron Roth, Bo Waggoner, Zhiwei Steven Wu
Date: 2019-02-06 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Classification, Decision Making Under Uncertainty, Fairness, General Classification, Multi-Armed Bandits

We study an online classification problem with partial feedback in which individuals arrive one at a time from a fixed but unknown distribution, and must be classified as positive or negative. Our algorithm only observes the true label of an individual if they are given a positive classification. This setting captures many classification problems for which fairness is a concern: for example, in criminal recidivism prediction, recidivism is only observed if the inmate is released; in lending applications, loan repayment is only observed if the loan is granted. We require that our algorithms satisfy common statistical fairness constraints (such as equalizing false positive or negative rates -- introduced as "equal opportunity" in Hardt et al. (2016)) at every round, with respect to the underlying distribution. We give upper and lower bounds characterizing the cost of this constraint in terms of the regret rate (and show that it is mild), and give an oracle efficient algorithm that achieves the upper bound.
Dynamic Pricing for Airline Ancillaries with Customer Context
Authors: Naman Shukla, Arinbjörn Kolbeinsson, Ken Otwell, Lavanya Marla, Kartik Yellepeddi
Date: 2019-02-06 00:00:00

Ancillaries have become a major source of revenue and profitability in the
travel industry. Yet, conventional pricing strategies are based on business
rules that are poorly optimized and do not respond to changing market
conditions. This paper describes the dynamic pricing model developed by Deepair
solutions, an AI technology provider for travel suppliers. We present a pricing
model that provides dynamic pricing recommendations specific to each customer
interaction and optimizes expected revenue per customer. The unique nature of
personalized pricing provides the opportunity to search over the market space
to find the optimal price-point of each ancillary for each customer, without
violating customer privacy. In this paper, we present and compare three
approaches for dynamic pricing of ancillaries, with increasing levels of
sophistication: (1) a two-stage forecasting and optimization model using a
logistic mapping function; (2) a two-stage model that uses a deep neural
network for forecasting, coupled with a revenue maximization technique using
discrete exhaustive search; (3) a single-stage end-to-end deep neural network
that recommends the optimal price. We describe the performance of these models
based on both offline and online evaluations. We also measure the real-world
business impact of these approaches by deploying them in an A/B test on an
airline's internet booking website. We show that traditional machine learning
techniques outperform human rule-based approaches in an online setting by
improving conversion by 36% and revenue per offer by 10%. We also provide
results for our offline experiments which show that deep learning algorithms
outperform traditional machine learning techniques for this problem. Our
end-to-end deep learning model is currently being deployed by the airline in
their booking system.