pwc-archive/papers-with-abstracts huggingface dataset:
Fuzzy Based Implicit Sentiment Analysis on Quantitative Sentences
Authors: Amir Hossein Yazdavar, Monireh Ebrahimi, Naomie Salim
Date: 2017-01-03 00:00:00
Tasks: Opinion Mining, Sentiment Analysis, Sentiment Classification

With the rapid growth of social media on the web, emotional polarity
computation has become a flourishing frontier in the text mining community.
However, it is challenging to understand the latest trends and summarize the
state or general opinions about products due to the big diversity and size of
social media data and this creates the need of automated and real time opinion
extraction and mining. On the other hand, the bulk of current research has been
devoted to study the subjective sentences which contain opinion keywords and
limited work has been reported for objective statements that imply sentiment.
In this paper, fuzzy based knowledge engineering model has been developed for
sentiment classification of special group of such sentences including the
change or deviation from desired range or value. Drug reviews are the rich
source of such statements. Therefore, in this research, some experiments were
carried out on patient's reviews on several different cholesterol lowering
drugs to determine their sentiment polarity. The main conclusion through this
study is, in order to increase the accuracy level of existing drug opinion
mining systems, objective sentences which imply opinion should be taken into
account. Our experimental results demonstrate that our proposed model obtains
over 72 percent F1 value.
Constrained Deep Weak Supervision for Histopathology Image Segmentation
Authors: Zhipeng Jia, Xingyi Huang, Eric I-Chao Chang, Yan Xu
Date: 2017-01-03 00:00:00
Tasks: Image Segmentation, Multiple Instance Learning, Semantic Segmentation, Weakly-supervised Learning

In this paper, we develop a new weakly-supervised learning algorithm to learn
to segment cancerous regions in histopathology images. Our work is under a
multiple instance learning framework (MIL) with a new formulation, deep weak
supervision (DWS); we also propose an effective way to introduce constraints to
our neural networks to assist the learning process. The contributions of our
algorithm are threefold: (1) We build an end-to-end learning system that
segments cancerous regions with fully convolutional networks (FCN) in which
image-to-image weakly-supervised learning is performed. (2) We develop a deep
week supervision formulation to exploit multi-scale learning under weak
supervision within fully convolutional networks. (3) Constraints about positive
instances are introduced in our approach to effectively explore additional
weakly-supervised information that is easy to obtain and enjoys a significant
boost to the learning process. The proposed algorithm, abbreviated as DWS-MIL,
is easy to implement and can be trained efficiently. Our system demonstrates
state-of-the-art results on large-scale histopathology image datasets and can
be applied to various applications in medical imaging beyond histopathology
images such as MRI, CT, and ultrasound images.
How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation
Authors: Chia-Wei Liu, Ryan Lowe, Iulian V. Serban, Michael Noseworthy, Laurent Charlin, Joelle Pineau
Date: 2016-03-25 00:00:00
Proceeding: EMNLP 2016 11
Tasks: Machine Translation, Response Generation, Translation

We investigate evaluation metrics for dialogue response generation systems
where supervised labels, such as task completion, are not available. Recent
works in response generation have adopted metrics from machine translation to
compare a model's generated response to a single target response. We show that
these metrics correlate very weakly with human judgements in the non-technical
Twitter domain, and not at all in the technical Ubuntu domain. We provide
quantitative and qualitative results highlighting specific weaknesses in
existing metrics, and provide recommendations for future development of better
automatic evaluation metrics for dialogue systems.
Automated timetabling for small colleges and high schools using huge integer programs
Authors: Joshua S. Friedman
Date: 2016-12-28 00:00:00
Tasks: Scheduling

We formulate an integer program to solve a highly constrained academic
timetabling problem at the United States Merchant Marine Academy. The IP
instance that results from our real case study has approximately both 170,000
rows and columns and solves to optimality in 4--24 hours using a commercial
solver on a portable computer (near optimal feasible solutions were often found
in 4--12 hours). Our model is applicable to both high schools and small
colleges who wish to deviate from group scheduling. We also solve a necessary
preprocessing student subgrouping problem, which breaks up big groups of
students into small groups so they can optimally fit into small capacity
classes.
Subspace Learning From Bits
Authors: Yuejie Chi, Haoyu Fu
Date: 2014-07-23 00:00:00

Networked sensing, where the goal is to perform complex inference using a
large number of inexpensive and decentralized sensors, has become an
increasingly attractive research topic due to its applications in wireless
sensor networks and internet-of-things. To reduce the communication, sensing
and storage complexity, this paper proposes a simple sensing and estimation
framework to faithfully recover the principal subspace of high-dimensional data
streams using a collection of binary measurements from distributed sensors,
without transmitting the whole data. The binary measurements are designed to
indicate comparison outcomes of aggregated energy projections of the data
samples over pairs of randomly selected directions. When the covariance matrix
is a low-rank matrix, we propose a spectral estimator that recovers the
principal subspace of the covariance matrix as the subspace spanned by the top
eigenvectors of a properly designed surrogate matrix, which is provably
accurate as soon as the number of binary measurements is sufficiently large. An
adaptive rank selection strategy based on soft thresholding is also presented.
Furthermore, we propose a tailored spectral estimator when the covariance
matrix is additionally Toeplitz, and show reliable estimation can be obtained
from a substantially smaller number of binary measurements. Our results hold
even when a constant fraction of the binary measurements is randomly flipped.
Finally, we develop a low-complexity online algorithm to track the principal
subspace when new measurements arrive sequentially. Numerical examples are
provided to validate the proposed approach.
Clustering Signed Networks with the Geometric Mean of Laplacians
Authors: Pedro Mercado, Francesco Tudisco, Matthias Hein
Date: 2017-01-03 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Clustering

Signed networks allow to model positive and negative relationships. We
analyze existing extensions of spectral clustering to signed networks. It turns
out that existing approaches do not recover the ground truth clustering in
several situations where either the positive or the negative network structures
contain no noise. Our analysis shows that these problems arise as existing
approaches take some form of arithmetic mean of the Laplacians of the positive
and negative part. As a solution we propose to use the geometric mean of the
Laplacians of positive and negative part and show that it outperforms the
existing approaches. While the geometric mean of matrices is computationally
expensive, we show that eigenvectors of the geometric mean can be computed
efficiently, leading to a numerical scheme for sparse matrices which is of
independent interest.
Meta-Unsupervised-Learning: A supervised approach to unsupervised learning
Authors: Vikas K. Garg, Adam Tauman Kalai
Date: 2016-12-29 00:00:00
Tasks: Clustering, Decision Making, Outlier Detection

We introduce a new paradigm to investigate unsupervised learning, reducing
unsupervised learning to supervised learning. Specifically, we mitigate the
subjectivity in unsupervised decision-making by leveraging knowledge acquired
from prior, possibly heterogeneous, supervised learning tasks. We demonstrate
the versatility of our framework via comprehensive expositions and detailed
experiments on several unsupervised problems such as (a) clustering, (b)
outlier detection, and (c) similarity prediction under a common umbrella of
meta-unsupervised-learning. We also provide rigorous PAC-agnostic bounds to
establish the theoretical foundations of our framework, and show that our
framing of meta-clustering circumvents Kleinberg's impossibility theorem for
clustering.
Evaluating semantic models with word-sentence relatedness
Authors: Kimberly Glasgow, Matthew Roos, Amy Haufler, Mark Chevillet, Michael Wolmetz
Date: 2016-03-23 00:00:00
Tasks: Implicatures, Semantic Similarity, Semantic Textual Similarity, Sentence, STS

Semantic textual similarity (STS) systems are designed to encode and evaluate
the semantic similarity between words, phrases, sentences, and documents. One
method for assessing the quality or authenticity of semantic information
encoded in these systems is by comparison with human judgments. A data set for
evaluating semantic models was developed consisting of 775 English
word-sentence pairs, each annotated for semantic relatedness by human raters
engaged in a Maximum Difference Scaling (MDS) task, as well as a faster
alternative task. As a sample application of this relatedness data,
behavior-based relatedness was compared to the relatedness computed via four
off-the-shelf STS models: n-gram, Latent Semantic Analysis (LSA), Word2Vec, and
UMBC Ebiquity. Some STS models captured much of the variance in the human
judgments collected, but they were not sensitive to the implicatures and
entailments that were processed and considered by the participants. All text
stimuli and judgment data have been made freely available.
Pyndri: a Python Interface to the Indri Search Engine
Authors: Christophe Van Gysel, Evangelos Kanoulas, Maarten de Rijke
Date: 2017-01-03 00:00:00

We introduce pyndri, a Python interface to the Indri search engine. Pyndri
allows to access Indri indexes from Python at two levels: (1) dictionary and
tokenized document collection, (2) evaluating queries on the index. We hope
that with the release of pyndri, we will stimulate reproducible, open and
fast-paced IR research.
Labeling Topics with Images using Neural Networks
Authors: Nikolaos Aletras, Arpit Mittal
Date: 2016-08-01 00:00:00
Tasks: Re-Ranking, Topic Models

Topics generated by topic models are usually represented by lists of $t$
terms or alternatively using short phrases and images. The current
state-of-the-art work on labeling topics using images selects images by
re-ranking a small set of candidates for a given topic. In this paper, we
present a more generic method that can estimate the degree of association
between any arbitrary pair of an unseen topic and image using a deep neural
network. Our method has better runtime performance $O(n)$ compared to $O(n^2)$
for the current state-of-the-art method, and is also significantly more
accurate.
On (Commercial) Benefits of Automatic Text Summarization Systems in the News Domain: A Case of Media Monitoring and Media Response Analysis
Authors: Pashutan Modaresi, Philipp Gross, Siavash Sefidrodi, Mirja Eckhof, Stefan Conrad
Date: 2017-01-03 00:00:00
Tasks: Text Summarization

In this work, we present the results of a systematic study to investigate the
(commercial) benefits of automatic text summarization systems in a real world
scenario. More specifically, we define a use case in the context of media
monitoring and media response analysis and claim that even using a simple
query-based extractive approach can dramatically save the processing time of
the employees without significantly reducing the quality of their work.
Image denoising using group sparsity residual and external nonlocal self-similarity prior
Authors: Zhiyuan Zha, Xinggan Zhang, Qiong Wang, Yechao Bai, Lan Tang
Date: 2017-01-03 00:00:00
Tasks: Deblurring, Denoising, Image Denoising

Nonlocal image representation has been successfully used in many
image-related inverse problems including denoising, deblurring and deblocking.
However, a majority of reconstruction methods only exploit the nonlocal
self-similarity (NSS) prior of the degraded observation image, it is very
challenging to reconstruct the latent clean image. In this paper we propose a
novel model for image denoising via group sparsity residual and external NSS
prior. To boost the performance of image denoising, the concept of group
sparsity residual is proposed, and thus the problem of image denoising is
transformed into one that reduces the group sparsity residual. Due to the fact
that the groups contain a large amount of NSS information of natural images, we
obtain a good estimation of the group sparse coefficients of the original image
by the external NSS prior based on Gaussian Mixture model (GMM) learning and
the group sparse coefficients of noisy image is used to approximate the
estimation. Experimental results have demonstrated that the proposed method not
only outperforms many state-of-the-art methods, but also delivers the best
qualitative denoising results with finer details and less ringing artifacts.
Mixed one-bit compressive sensing with applications to overexposure correction for CT reconstruction
Authors: Xiaolin Huang, Yan Xia, Lei Shi, Yixing Huang, Ming Yan, Joachim Hornegger, Andreas Maier
Date: 2017-01-03 00:00:00
Tasks: Compressive Sensing, Computed Tomography (CT), CT Reconstruction, Quantization

When a measurement falls outside the quantization or measurable range, it
becomes saturated and cannot be used in classical reconstruction methods. For
example, in C-arm angiography systems, which provide projection radiography,
fluoroscopy, digital subtraction angiography, and are widely used for medical
diagnoses and interventions, the limited dynamic range of C-arm flat detectors
leads to overexposure in some projections during an acquisition, such as
imaging relatively thin body parts (e.g., the knee). Aiming at overexposure
correction for computed tomography (CT) reconstruction, we in this paper
propose a mixed one-bit compressive sensing (M1bit-CS) to acquire information
from both regular and saturated measurements. This method is inspired by the
recent progress on one-bit compressive sensing, which deals with only sign
observations. Its successful applications imply that information carried by
saturated measurements is useful to improve recovery quality. For the proposed
M1bit-CS model, alternating direction methods of multipliers is developed and
an iterative saturation detection scheme is established. Then we evaluate
M1bit-CS on one-dimensional signal recovery tasks. In some experiments, the
performance of the proposed algorithms on mixed measurements is almost the same
as recovery on unsaturated ones with the same amount of measurements. Finally,
we apply the proposed method to overexposure correction for CT reconstruction
on a phantom and a simulated clinical image. The results are promising, as the
typical streaking artifacts and capping artifacts introduced by saturated
projection data are effectively reduced, yielding significant error reduction
compared with existing algorithms based on extrapolation.
New Methods of Enhancing Prediction Accuracy in Linear Models with Missing Data
Authors: Mohammad Amin Fakharian, Ashkan Esmaeili, Farokh Marvasti
Date: 2017-01-03 00:00:00
Tasks: Prediction

In this paper, prediction for linear systems with missing information is
investigated. New methods are introduced to improve the Mean Squared Error
(MSE) on the test set in comparison to state-of-the-art methods, through
appropriate tuning of Bias-Variance trade-off. First, the use of proposed Soft
Weighted Prediction (SWP) algorithm and its efficacy are depicted and compared
to previous works for non-missing scenarios. The algorithm is then modified and
optimized for missing scenarios. It is shown that controlled over-fitting by
suggested algorithms will improve prediction accuracy in various cases.
Simulation results approve our heuristics in enhancing the prediction accuracy.
Caveats on Bayesian and hidden-Markov models (v2.8)
Authors: Lambert Schomaker
Date: 2016-08-18 00:00:00
Tasks: General Classification, Handwriting Recognition

This paper describes a number of fundamental and practical problems in the
application of hidden-Markov models and Bayes when applied to cursive-script
recognition. Several problems, however, will have an effect in other
application areas. The most fundamental problem is the propagation of error in
the product of probabilities. This is a common and pervasive problem which
deserves more attention. On the basis of Monte Carlo modeling, tables for the
expected relative error are given. It seems that it is distributed according to
a continuous Poisson distribution over log probabilities. A second essential
problem is related to the appropriateness of the Markov assumption. Basic tests
will reveal whether a problem requires modeling of the stochastics of
seriality, at all. Examples are given of lexical encodings which cover 95-99%
classification accuracy of a lexicon, with removed sequence information, for
several European languages. Finally, a summary of results on a non- Bayes,
non-Markov method in handwriting recognition are presented, with very
acceptable results and minimal modeling or training requirements using
nearest-mean classification.
Ambiguity and Incomplete Information in Categorical Models of Language
Authors: Dan Marsden
Date: 2017-01-03 00:00:00

We investigate notions of ambiguity and partial information in categorical
distributional models of natural language. Probabilistic ambiguity has
previously been studied using Selinger's CPM construction. This construction
works well for models built upon vector spaces, as has been shown in quantum
computational applications. Unfortunately, it doesn't seem to provide a
satisfactory method for introducing mixing in other compact closed categories
such as the category of sets and binary relations. We therefore lack a uniform
strategy for extending a category to model imprecise linguistic information.
  In this work we adopt a different approach. We analyze different forms of
ambiguous and incomplete information, both with and without quantitative
probabilistic data. Each scheme then corresponds to a suitable enrichment of
the category in which we model language. We view different monads as
encapsulating the informational behaviour of interest, by analogy with their
use in modelling side effects in computation. Previous results of Jacobs then
allow us to systematically construct suitable bases for enrichment.
  We show that we can freely enrich arbitrary dagger compact closed categories
in order to capture all the phenomena of interest, whilst retaining the
important dagger compact closed structure. This allows us to construct a model
with real convex combination of binary relations that makes non-trivial use of
the scalars. Finally we relate our various different enrichments, showing that
finite subconvex algebra enrichment covers all the effects under consideration.
Semidefinite tests for latent causal structures
Authors: Aditya Kela, Kai von Prillwitz, Johan Aberg, Rafael Chaves, David Gross
Date: 2017-01-03 00:00:00
Tasks: Causal Inference

Testing whether a probability distribution is compatible with a given
Bayesian network is a fundamental task in the field of causal inference, where
Bayesian networks model causal relations. Here we consider the class of causal
structures where all correlations between observed quantities are solely due to
the influence from latent variables. We show that each model of this type
imposes a certain signature on the observable covariance matrix in terms of a
particular decomposition into positive semidefinite components. This signature,
and thus the underlying hypothetical latent structure, can be tested in a
computationally efficient manner via semidefinite programming. This stands in
stark contrast with the algebraic geometric tools required if the full
observable probability distribution is taken into account. The semidefinite
test is compared with tests based on entropic inequalities.
Automatic sleep monitoring using ear-EEG
Authors: Takashi Nakamura, Valentin Goverdovsky, Mary J. Morrell, Danilo P. Mandic
Date: 2017-01-03 00:00:00
Tasks: EEG, Electroencephalogram (EEG), General Classification

The monitoring of sleep patterns without patient's inconvenience or
involvement of a medical specialist is a clinical question of significant
importance. To this end, we propose an automatic sleep stage monitoring system
based on an affordable, unobtrusive, discreet, and long-term wearable in-ear
sensor for recording the Electroencephalogram (ear-EEG). The selected features
for sleep pattern classification from a single ear-EEG channel include the
spectral edge frequency (SEF) and multi- scale fuzzy entropy (MSFE), a
structural complexity feature. In this preliminary study, the manually scored
hypnograms from simultaneous scalp-EEG and ear-EEG recordings of four subjects
are used as labels for two analysis scenarios: 1) classification of ear-EEG
hypnogram labels from ear-EEG recordings and 2) prediction of scalp-EEG
hypnogram labels from ear-EEG recordings. We consider both 2-class and 4-class
sleep scoring, with the achieved accuracies ranging from 78.5 % to 95.2 % for
ear-EEG labels predicted from ear-EEG, and 76.8 % to 91.8 % for scalp-EEG
labels predicted from ear-EEG. The corresponding kappa coefficients, which
range from 0.64 to 0.83 for Scenario 1 and from 0.65 to 0.80 for Scenario 2,
indicate a Substantial to Almost Perfect agreement, thus proving the
feasibility of in-ear sensing for sleep monitoring in the community.
From Preference-Based to Multiobjective Sequential Decision-Making
Authors: Paul Weng
Date: 2017-01-03 00:00:00
Tasks: Decision Making, Sequential Decision Making

In this paper, we present a link between preference-based and multiobjective
sequential decision-making. While transforming a multiobjective problem to a
preference-based one is quite natural, the other direction is a bit less
obvious. We present how this transformation (from preference-based to
multiobjective) can be done under the classic condition that preferences over
histories can be represented by additively decomposable utilities and that the
decision criterion to evaluate policies in a state is based on expectation.
This link yields a new source of multiobjective sequential decision-making
problems (i.e., when reward values are unknown) and justifies the use of
solving methods developed in one setting in the other one.
Finding Risk-Averse Shortest Path with Time-dependent Stochastic Costs
Authors: Dajian Li, Paul Weng, Orkun Karabasoglu
Date: 2017-01-03 00:00:00

In this paper, we tackle the problem of risk-averse route planning in a
transportation network with time-dependent and stochastic costs. To solve this
problem, we propose an adaptation of the A* algorithm that accommodates any
risk measure or decision criterion that is monotonic with first-order
stochastic dominance. We also present a case study of our algorithm on the
Manhattan, NYC, transportation network.