pwc-archive/papers-with-abstracts huggingface dataset:
Constraint matrix factorization for space variant PSFs field restoration
Authors: F. M. Ngolè Mboula, J. -L. Starck, K. Okumura, J. Amiaux, P. Hudelot
Date: 2016-08-29 00:00:00
Tasks: Dimensionality Reduction, Super-Resolution

Context: in large-scale spatial surveys, the Point Spread Function (PSF)
varies across the instrument field of view (FOV). Local measurements of the
PSFs are given by the isolated stars images. Yet, these estimates may not be
directly usable for post-processings because of the observational noise and
potentially the aliasing. Aims: given a set of aliased and noisy stars images
from a telescope, we want to estimate well-resolved and noise-free PSFs at the
observed stars positions, in particular, exploiting the spatial correlation of
the PSFs across the FOV. Contributions: we introduce RCA (Resolved Components
Analysis) which is a noise-robust dimension reduction and super-resolution
method based on matrix factorization. We propose an original way of using the
PSFs spatial correlation in the restoration process through sparsity. The
introduced formalism can be applied to correlated data sets with respect to any
euclidean parametric space. Results: we tested our method on simulated
monochromatic PSFs of Euclid telescope (launch planned for 2020). The proposed
method outperforms existing PSFs restoration and dimension reduction methods.
We show that a coupled sparsity constraint on individual PSFs and their spatial
distribution yields a significant improvement on both the restored PSFs shapes
and the PSFs subspace identification, in presence of aliasing. Perspectives:
RCA can be naturally extended to account for the wavelength dependency of the
PSFs.
A Dictionary-based Approach to Racism Detection in Dutch Social Media
Authors: Stéphan Tulkens, Lisa Hilte, Elise Lodewyckx, Ben Verhoeven, Walter Daelemans
Date: 2016-08-31 00:00:00

We present a dictionary-based approach to racism detection in Dutch social
media comments, which were retrieved from two public Belgian social media sites
likely to attract racist reactions. These comments were labeled as racist or
non-racist by multiple annotators. For our approach, three discourse
dictionaries were created: first, we created a dictionary by retrieving
possibly racist and more neutral terms from the training data, and then
augmenting these with more general words to remove some bias. A second
dictionary was created through automatic expansion using a \texttt{word2vec}
model trained on a large corpus of general Dutch text. Finally, a third
dictionary was created by manually filtering out incorrect expansions. We
trained multiple Support Vector Machines, using the distribution of words over
the different categories in the dictionaries as features. The best-performing
model used the manually cleaned dictionary and obtained an F-score of 0.46 for
the racist class on a test set consisting of unseen Dutch comments, retrieved
from the same sites used for the training set. The automated expansion of the
dictionary only slightly boosted the model's performance, and this increase in
performance was not statistically significant. The fact that the coverage of
the expanded dictionaries did increase indicates that the words that were
automatically added did occur in the corpus, but were not able to meaningfully
impact performance. The dictionaries, code, and the procedure for requesting
the corpus are available at: https://github.com/clips/hades
A Programming Language With a POMDP Inside
Authors: Christopher H. Lin, Mausam, Daniel S. Weld
Date: 2016-08-31 00:00:00

We present POAPS, a novel planning system for defining Partially Observable
Markov Decision Processes (POMDPs) that abstracts away from POMDP details for
the benefit of non-expert practitioners. POAPS includes an expressive adaptive
programming language based on Lisp that has constructs for choice points that
can be dynamically optimized. Non-experts can use our language to write
adaptive programs that have partially observable components without needing to
specify belief/hidden states or reason about probabilities. POAPS is also a
compiler that defines and performs the transformation of any program written in
our language into a POMDP with control knowledge. We demonstrate the generality
and power of POAPS in the rapidly growing domain of human computation by
describing its expressiveness and simplicity by writing several POAPS programs
for common crowdsourcing tasks.
Measuring Machine Intelligence Through Visual Question Answering
Authors: C. Lawrence Zitnick, Aishwarya Agrawal, Stanislaw Antol, Margaret Mitchell, Dhruv Batra, Devi Parikh
Date: 2016-08-31 00:00:00
Tasks: Image Captioning, Question Answering, Visual Question Answering, Visual Question Answering (VQA)

As machines have become more intelligent, there has been a renewed interest
in methods for measuring their intelligence. A common approach is to propose
tasks for which a human excels, but one which machines find difficult. However,
an ideal task should also be easy to evaluate and not be easily gameable. We
begin with a case study exploring the recently popular task of image captioning
and its limitations as a task for measuring machine intelligence. An
alternative and more promising task is Visual Question Answering that tests a
machine's ability to reason about language and vision. We describe a dataset
unprecedented in size created for the task that contains over 760,000 human
generated questions about images. Using around 10 million human generated
answers, machines may be easily evaluated.
Engagement Detection in Meetings
Authors: Maria Frank, Ghassem Tofighi, Haisong Gu, Renate Fruchter
Date: 2016-08-31 00:00:00

Group meetings are frequent business events aimed to develop and conduct
project work, such as Big Room design and construction project meetings. To be
effective in these meetings, participants need to have an engaged mental state.
The mental state of participants however, is hidden from other participants,
and thereby difficult to evaluate. Mental state is understood as an inner
process of thinking and feeling, that is formed of a conglomerate of mental
representations and propositional attitudes. There is a need to create
transparency of these hidden states to understand, evaluate and influence them.
Facilitators need to evaluate the meeting situation and adjust for higher
engagement and productivity. This paper presents a framework that defines a
spectrum of engagement states and an array of classifiers aimed to detect the
engagement state of participants in real time. The Engagement Framework
integrates multi-modal information from 2D and 3D imaging and sound. Engagement
is detected and evaluated at participants and aggregated at group level. We use
empirical data collected at the lab of Konica Minolta, Inc. to test initial
applications of this framework. The paper presents examples of the tested
engagement classifiers, which are based on research in psychology,
communication, and human computer interaction. Their accuracy is illustrated in
dyadic interaction for engagement detection. In closing we discuss the
potential extension to complex group collaboration settings and future feedback
implementations.
Reconstructing parameters of spreading models from partial observations
Authors: Andrey Y. Lokhov
Date: 2016-08-31 00:00:00
Proceeding: NeurIPS 2016

Spreading processes are often modelled as a stochastic dynamics occurring on
top of a given network with edge weights corresponding to the transmission
probabilities. Knowledge of veracious transmission probabilities is essential
for prediction, optimization, and control of diffusion dynamics. Unfortunately,
in most cases the transmission rates are unknown and need to be reconstructed
from the spreading data. Moreover, in realistic settings it is impossible to
monitor the state of each node at every time, and thus the data is highly
incomplete. We introduce an efficient dynamic message-passing algorithm, which
is able to reconstruct parameters of the spreading model given only partial
information on the activation times of nodes in the network. The method is
generalizable to a large class of dynamic models, as well to the case of
temporal graphs.
Leveraging Sentence-level Information with Encoder LSTM for Semantic Slot Filling
Authors: Gakuto Kurata, Bing Xiang, Bo-Wen Zhou, Mo Yu
Date: 2016-01-07 00:00:00
Proceeding: EMNLP 2016 11
Tasks: Natural Language Understanding, Sentence, slot-filling, Slot Filling

Recurrent Neural Network (RNN) and one of its specific architectures, Long
Short-Term Memory (LSTM), have been widely used for sequence labeling. In this
paper, we first enhance LSTM-based sequence labeling to explicitly model label
dependencies. Then we propose another enhancement to incorporate the global
information spanning over the whole input sequence. The latter proposed method,
encoder-labeler LSTM, first encodes the whole input sequence into a fixed
length vector with the encoder LSTM, and then uses this encoded vector as the
initial state of another LSTM for sequence labeling. Combining these methods,
we can predict the label sequence with considering label dependencies and
information of whole input sequence. In the experiments of a slot filling task,
which is an essential component of natural language understanding, with using
the standard ATIS corpus, we achieved the state-of-the-art F1-score of 95.66%.
Joint Estimation of Multiple Dependent Gaussian Graphical Models with Applications to Mouse Genomics
Authors: Yuying Xie, Yufeng Liu, William Valdar
Date: 2016-08-30 00:00:00

Gaussian graphical models are widely used to represent conditional dependence
among random variables. In this paper, we propose a novel estimator for data
arising from a group of Gaussian graphical models that are themselves
dependent. A motivating example is that of modeling gene expression collected
on multiple tissues from the same individual: here the multivariate outcome is
affected by dependencies acting not only at the level of the specific tissues,
but also at the level of the whole body; existing methods that assume
independence among graphs are not applicable in this case. To estimate multiple
dependent graphs, we decompose the problem into two graphical layers: the
systemic layer, which affects all outcomes and thereby induces cross- graph
dependence, and the category-specific layer, which represents graph-specific
variation. We propose a graphical EM technique that estimates both layers
jointly, establish estimation consistency and selection sparsistency of the
proposed estimator, and confirm by simulation that the EM method is superior to
a simple one-step method. We apply our technique to mouse genomics data and
obtain biologically plausible results.
Visualisation of Survey Responses using Self-Organising Maps: A Case Study on Diabetes Self-care Factors
Authors: Santosh Tirunagari, Simon Bull, Samaneh Kouchaki, Deborah Cooke, Norman Poh
Date: 2016-08-30 00:00:00
Tasks: Survey

Due to the chronic nature of diabetes, patient self-care factors play an
important role in any treatment plan. In order to understand the behaviour of
patients in response to medical advice on self-care, clinicians often conduct
cross-sectional surveys. When analysing the survey data, statistical machine
learning methods can potentially provide additional insight into the data
either through deeper understanding of the patterns present or making
information available to clinicians in an intuitive manner. In this study, we
use self-organising maps (SOMs) to visualise the responses of patients who
share similar responses to survey questions, with the goal of helping
clinicians understand how patients are managing their treatment and where
action should be taken. The principle behavioural patterns revealed through
this are that: patients who take the correct dose of insulin also tend to take
their injections at the correct time, patients who eat on time also tend to
correctly manage their food portions and patients who check their blood glucose
with a monitor also tend to adjust their insulin dosage and carry snacks to
counter low blood glucose. The identification of these positive behavioural
patterns can also help to inform treatment by exploiting their negative
corollaries.
Game-Theoretic Modeling of Driver and Vehicle Interactions for Verification and Validation of Autonomous Vehicle Control Systems
Authors: Nan Li, Dave Oyler, Mengxuan Zhang, Yildiray Yildiz, Ilya Kolmanovsky, Anouck Girard
Date: 2016-08-30 00:00:00
Tasks: Autonomous Driving

Autonomous driving has been the subject of increased interest in recent years
both in industry and in academia. Serious efforts are being pursued to address
legal, technical and logistical problems and make autonomous cars a viable
option for everyday transportation. One significant challenge is the time and
effort required for the verification and validation of the decision and control
algorithms employed in these vehicles to ensure a safe and comfortable driving
experience. Hundreds of thousands of miles of driving tests are required to
achieve a well calibrated control system that is capable of operating an
autonomous vehicle in an uncertain traffic environment where multiple
interactions between vehicles and drivers simultaneously occur. Traffic
simulators where these interactions can be modeled and represented with
reasonable fidelity can help decrease the time and effort necessary for the
development of the autonomous driving control algorithms by providing a venue
where acceptable initial control calibrations can be achieved quickly and
safely before actual road tests. In this paper, we present a game theoretic
traffic model that can be used to 1) test and compare various autonomous
vehicle decision and control systems and 2) calibrate the parameters of an
existing control system. We demonstrate two example case studies, where, in the
first case, we test and quantitatively compare two autonomous vehicle control
systems in terms of their safety and performance, and, in the second case, we
optimize the parameters of an autonomous vehicle control system, utilizing the
proposed traffic model and simulation environment.
Applying Naive Bayes Classification to Google Play Apps Categorization
Authors: Babatunde Olabenjo
Date: 2016-08-30 00:00:00
Tasks: Classification, Document Classification, General Classification, Sentiment Analysis, Spam detection

There are over one million apps on Google Play Store and over half a million
publishers. Having such a huge number of apps and developers can pose a
challenge to app users and new publishers on the store. Discovering apps can be
challenging if apps are not correctly published in the right category, and, in
turn, reduce earnings for app developers. Additionally, with over 41 categories
on Google Play Store, deciding on the right category to publish an app can be
challenging for developers due to the number of categories they have to choose
from. Machine Learning has been very useful, especially in classification
problems such sentiment analysis, document classification and spam detection.
These strategies can also be applied to app categorization on Google Play Store
to suggest appropriate categories for app publishers using details from their
application.
  In this project, we built two variations of the Naive Bayes classifier using
open metadata from top developer apps on Google Play Store in other to classify
new apps on the store. These classifiers are then evaluated using various
evaluation methods and their results compared against each other. The results
show that the Naive Bayes algorithm performs well for our classification
problem and can potentially automate app categorization for Android app
publishers on Google Play Store
A Framework for Fast Image Deconvolution with Incomplete Observations
Authors: Miguel Simões, Luis B. Almeida, José Bioucas-Dias, Jocelyn Chanussot
Date: 2016-02-03 00:00:00
Tasks: Demosaicking, Image Deconvolution

In image deconvolution problems, the diagonalization of the underlying
operators by means of the FFT usually yields very large speedups. When there
are incomplete observations (e.g., in the case of unknown boundaries), standard
deconvolution techniques normally involve non-diagonalizable operators,
resulting in rather slow methods, or, otherwise, use inexact convolution
models, resulting in the occurrence of artifacts in the enhanced images. In
this paper, we propose a new deconvolution framework for images with incomplete
observations that allows us to work with diagonalized convolution operators,
and therefore is very fast. We iteratively alternate the estimation of the
unknown pixels and of the deconvolved image, using, e.g., an FFT-based
deconvolution method. This framework is an efficient, high-quality alternative
to existing methods of dealing with the image boundaries, such as edge
tapering. It can be used with any fast deconvolution method. We give an example
in which a state-of-the-art method that assumes periodic boundary conditions is
extended, through the use of this framework, to unknown boundary conditions.
Furthermore, we propose a specific implementation of this framework, based on
the alternating direction method of multipliers (ADMM). We provide a proof of
convergence for the resulting algorithm, which can be seen as a "partial" ADMM,
in which not all variables are dualized. We report experimental comparisons
with other primal-dual methods, where the proposed one performed at the level
of the state of the art. Four different kinds of applications were tested in
the experiments: deconvolution, deconvolution with inpainting, superresolution,
and demosaicing, all with unknown boundaries.
Load Disaggregation Based on Aided Linear Integer Programming
Authors: Md. Zulfiquar Ali Bhotto, Stephen Makonin, Ivan V. Bajic
Date: 2016-03-24 00:00:00

Load disaggregation based on aided linear integer programming (ALIP) is
proposed. We start with a conventional linear integer programming (IP) based
disaggregation and enhance it in several ways. The enhancements include
additional constraints, correction based on a state diagram, median filtering,
and linear programming-based refinement. With the aid of these enhancements,
the performance of IP-based disaggregation is significantly improved. The
proposed ALIP system relies only on the instantaneous load samples instead of
waveform signatures, and hence does not crucially depend on high sampling
frequency. Experimental results show that the proposed ALIP system performs
better than the conventional IP-based load disaggregation system.
Language Detection For Short Text Messages In Social Media
Authors: Ivana Balazevic, Mikio Braun, Klaus-Robert Müller
Date: 2016-08-30 00:00:00

With the constant growth of the World Wide Web and the number of documents in
different languages accordingly, the need for reliable language detection tools
has increased as well. Platforms such as Twitter with predominantly short texts
are becoming important information resources, which additionally imposes the
need for short texts language detection algorithms. In this paper, we show how
incorporating personalized user-specific information into the language
detection algorithm leads to an important improvement of detection results. To
choose the best algorithm for language detection for short text messages, we
investigate several machine learning approaches. These approaches include the
use of the well-known classifiers such as SVM and logistic regression, a
dictionary based approach, and a probabilistic model based on modified
Kneser-Ney smoothing. Furthermore, the extension of the probabilistic model to
include additional user-specific information such as evidence accumulation per
user and user interface language is explored, with the goal of improving the
classification performance. The proposed approaches are evaluated on randomly
collected Twitter data containing Latin as well as non-Latin alphabet languages
and the quality of the obtained results is compared, followed by the selection
of the best performing algorithm. This algorithm is then evaluated against two
already existing general language detection tools: Chromium Compact Language
Detector 2 (CLD2) and langid, where our method significantly outperforms the
results achieved by both of the mentioned methods. Additionally, a preview of
benefits and possible applications of having a reliable language detection
algorithm is given.
Modelling Cyber-Security Experts' Decision Making Processes using Aggregation Operators
Authors: Simon Miller, Christian Wagner, Uwe Aickelin, Jonathan M. Garibaldi
Date: 2016-08-30 00:00:00
Tasks: Decision Making, Evolutionary Algorithms

An important role carried out by cyber-security experts is the assessment of
proposed computer systems, during their design stage. This task is fraught with
difficulties and uncertainty, making the knowledge provided by human experts
essential for successful assessment. Today, the increasing number of
progressively complex systems has led to an urgent need to produce tools that
support the expert-led process of system-security assessment. In this research,
we use weighted averages (WAs) and ordered weighted averages (OWAs) with
evolutionary algorithms (EAs) to create aggregation operators that model parts
of the assessment process. We show how individual overall ratings for security
components can be produced from ratings of their characteristics, and how these
individual overall ratings can be aggregated to produce overall rankings of
potential attacks on a system. As well as the identification of salient attacks
and weak points in a prospective system, the proposed method also highlights
which factors and security components contribute most to a component's
difficulty and attack ranking respectively. A real world scenario is used in
which experts were asked to rank a set of technical attacks, and to answer a
series of questions about the security components that are the subject of the
attacks. The work shows how finding good aggregation operators, and identifying
important components and factors of a cyber-security problem can be automated.
The resulting operators have the potential for use as decision aids for systems
designers and cyber-security experts, increasing the amount of assessment that
can be achieved with the limited resources available.
New Methods to Improve Large-Scale Microscopy Image Analysis with Prior Knowledge and Uncertainty
Authors: Johannes Stegmaier
Date: 2016-08-30 00:00:00

Multidimensional imaging techniques provide powerful ways to examine various
kinds of scientific questions. The routinely produced datasets in the
terabyte-range, however, can hardly be analyzed manually and require an
extensive use of automated image analysis. The present thesis introduces a new
concept for the estimation and propagation of uncertainty involved in image
analysis operators and new segmentation algorithms that are suitable for
terabyte-scale analyses of 3D+t microscopy images.