pwc-archive/papers-with-abstracts huggingface dataset:
Gradient Distribution Priors for Biomedical Image Processing
Authors: Yuanhao Gong, Ivo F. Sbalzarini
Date: 2014-08-13 00:00:00
Tasks: Denoising

Ill-posed inverse problems are commonplace in biomedical image processing.
Their solution typically requires imposing prior knowledge about the latent
ground truth. While this regularizes the problem to an extent where it can be
solved, it also biases the result toward the expected. With inappropriate
priors harming more than they use, it remains unclear what prior to use for a
given practical problem. Priors are hence mostly chosen in an {\em ad hoc} or
empirical fashion. We argue here that the gradient distribution of
natural-scene images may provide a versatile and well-founded prior for
biomedical images. We provide motivation for this choice from different points
of view, and we fully validate the resulting prior for use on biomedical images
by showing its stability and correlation with image quality. We then provide a
set of simple parametric models for the resulting prior, leading to
straightforward (quasi-)convex optimization problems for which we provide
efficient solver algorithms. We illustrate the use of the present models and
solvers in a variety of common image-processing tasks, including contrast
enhancement, noise level estimation, denoising, blind deconvolution,
zooming/up-sampling, and dehazing. In all cases we show that the present method
leads to results that are comparable to or better than the state of the art;
always using the same, simple prior. We conclude by discussing the limitations
and possible interpretations of the prior.
Friendly Artificial Intelligence: the Physics Challenge
Authors: Max Tegmark
Date: 2014-09-02 00:00:00

Relentless progress in artificial intelligence (AI) is increasingly raising
concerns that machines will replace humans on the job market, and perhaps
altogether. Eliezer Yudkowski and others have explored the possibility that a
promising future for humankind could be guaranteed by a superintelligent
"Friendly AI", designed to safeguard humanity and its values. I argue that,
from a physics perspective where everything is simply an arrangement of
elementary particles, this might be even harder than it appears. Indeed, it may
require thinking rigorously about the meaning of life: What is "meaning" in a
particle arrangement? What is "life"? What is the ultimate ethical imperative,
i.e., how should we strive to rearrange the particles of our Universe and shape
its future? If we fail to answer the last question rigorously, this future is
unlikely to contain humans.
Performance Analysis on Evolutionary Algorithms for the Minimum Label Spanning Tree Problem
Authors: Xinsheng Lai, Yuren Zhou, Jun He, Jun Zhang
Date: 2014-09-03 00:00:00
Tasks: Evolutionary Algorithms

Some experimental investigations have shown that evolutionary algorithms
(EAs) are efficient for the minimum label spanning tree (MLST) problem.
However, we know little about that in theory. As one step towards this issue,
we theoretically analyze the performances of the (1+1) EA, a simple version of
EAs, and a multi-objective evolutionary algorithm called GSEMO on the MLST
problem. We reveal that for the MLST$_{b}$ problem the (1+1) EA and GSEMO
achieve a $\frac{b+1}{2}$-approximation ratio in expected polynomial times of
$n$ the number of nodes and $k$ the number of labels. We also show that GSEMO
achieves a $(2ln(n))$-approximation ratio for the MLST problem in expected
polynomial time of $n$ and $k$. At the same time, we show that the (1+1) EA and
GSEMO outperform local search algorithms on three instances of the MLST
problem. We also construct an instance on which GSEMO outperforms the (1+1) EA.
Structured Low-Rank Matrix Factorization with Missing and Grossly Corrupted Observations
Authors: Fanhua Shang, Yuanyuan Liu, Hanghang Tong, James Cheng, Hong Cheng
Date: 2014-09-03 00:00:00
Tasks: Matrix Completion

Recovering low-rank and sparse matrices from incomplete or corrupted
observations is an important problem in machine learning, statistics,
bioinformatics, computer vision, as well as signal and image processing. In
theory, this problem can be solved by the natural convex joint/mixed
relaxations (i.e., l_{1}-norm and trace norm) under certain conditions.
However, all current provable algorithms suffer from superlinear per-iteration
cost, which severely limits their applicability to large-scale problems. In
this paper, we propose a scalable, provable structured low-rank matrix
factorization method to recover low-rank and sparse matrices from missing and
grossly corrupted data, i.e., robust matrix completion (RMC) problems, or
incomplete and grossly corrupted measurements, i.e., compressive principal
component pursuit (CPCP) problems. Specifically, we first present two
small-scale matrix trace norm regularized bilinear structured factorization
models for RMC and CPCP problems, in which repetitively calculating SVD of a
large-scale matrix is replaced by updating two much smaller factor matrices.
Then, we apply the alternating direction method of multipliers (ADMM) to
efficiently solve the RMC problems. Finally, we provide the convergence
analysis of our algorithm, and extend it to address general CPCP problems.
Experimental results verified both the efficiency and effectiveness of our
method compared with the state-of-the-art methods.
Augmented Neural Networks for Modelling Consumer Indebtness
Authors: Alexandros Ladas, Jonathan M. Garibaldi, Rodrigo Scarpel, Uwe Aickelin
Date: 2014-09-03 00:00:00

Consumer Debt has risen to be an important problem of modern societies,
generating a lot of research in order to understand the nature of consumer
indebtness, which so far its modelling has been carried out by statistical
models. In this work we show that Computational Intelligence can offer a more
holistic approach that is more suitable for the complex relationships an
indebtness dataset has and Linear Regression cannot uncover. In particular, as
our results show, Neural Networks achieve the best performance in modelling
consumer indebtness, especially when they manage to incorporate the significant
and experimentally verified results of the Data Mining process in the model,
exploiting the flexibility Neural Networks offer in designing their topology.
This novel method forms an elaborate framework to model Consumer indebtness
that can be extended to any other real world application.
Tuning a Multiple Classifier System for Side Effect Discovery using Genetic Algorithms
Authors: Jenna M. Reps, Uwe Aickelin, Jonathan M. Garibaldi
Date: 2014-09-03 00:00:00

In previous work, a novel supervised framework implementing a binary
classifier was presented that obtained excellent results for side effect
discovery. Interestingly, unique side effects were identified when different
binary classifiers were used within the framework, prompting the investigation
of applying a multiple classifier system. In this paper we investigate tuning a
side effect multiple classifying system using genetic algorithms. The results
of this research show that the novel framework implementing a multiple
classifying system trained using genetic algorithms can obtain a higher partial
area under the receiver operating characteristic curve than implementing a
single classifier. Furthermore, the framework is able to detect side effects
efficiently and obtains a low false positive rate.
Analysing Fuzzy Sets Through Combining Measures of Similarity and Distance
Authors: Josie McCulloch, Christian Wagner, Uwe Aickelin
Date: 2014-09-03 00:00:00

Reasoning with fuzzy sets can be achieved through measures such as similarity
and distance. However, these measures can often give misleading results when
considered independently, for example giving the same value for two different
pairs of fuzzy sets. This is particularly a problem where many fuzzy sets are
generated from real data, and while two different measures may be used to
automatically compare such fuzzy sets, it is difficult to interpret two
different results. This is especially true where a large number of fuzzy sets
are being compared as part of a reasoning system. This paper introduces a
method for combining the results of multiple measures into a single measure for
the purpose of analysing and comparing fuzzy sets. The combined measure
alleviates ambiguous results and aids in the automatic comparison of fuzzy
sets. The properties of the combined measure are given, and demonstrations are
presented with discussions on the advantages over using a single measure.
A Fuzzy Directional Distance Measure
Authors: Josie C. McCullochy, Chris J. Hinde, Christian Wagner, Uwe Aickelin
Date: 2014-09-03 00:00:00

The measure of distance between two fuzzy sets is a fundamental tool within
fuzzy set theory, however, distance measures currently within the literature
use a crisp value to represent the distance between fuzzy sets. A real valued
distance measure is developed into a fuzzy distance measure which better
reflects the uncertainty inherent in fuzzy sets and a fuzzy directional
distance measure is presented, which accounts for the direction of change
between fuzzy sets. A multiplicative version is explored as a full maximal
assignment is computationally intractable so an intermediate solution is
offered.
Variability of Behaviour in Electricity Load Profile Clustering; Who Does Things at the Same Time Each Day?
Authors: Ian Dent, Tony Craig, Uwe Aickelin, Tom Rodden
Date: 2014-09-03 00:00:00
Tasks: Clustering

UK electricity market changes provide opportunities to alter households'
electricity usage patterns for the benefit of the overall electricity network.
Work on clustering similar households has concentrated on daily load profiles
and the variability in regular household behaviours has not been considered.
Those households with most variability in regular activities may be the most
receptive to incentives to change timing.
  Whether using the variability of regular behaviour allows the creation of
more consistent groupings of households is investigated and compared with daily
load profile clustering. 204 UK households are analysed to find repeating
patterns (motifs). Variability in the time of the motif is used as the basis
for clustering households. Different clustering algorithms are assessed by the
consistency of the results.
  Findings show that variability of behaviour, using motifs, provides more
consistent groupings of households across different clustering algorithms and
allows for more efficient targeting of behaviour change interventions.
EVOC: A Computer Model of the Evolution of Culture
Authors: Liane Gabora
Date: 2013-10-01 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction, Diversity

EVOC is a computer model of the EVOlution of Culture. It consists of neural
network based agents that invent ideas for actions, and imitate neighbors'
actions. EVOC replicates using a different fitness function the results
obtained with an earlier model (MAV), including (1) an increase in mean fitness
of actions, and (2) an increase and then decrease in the diversity of actions.
Diversity of actions is positively correlated with number of needs, population
size and density, and with the erosion of borders between populations. Slowly
eroding borders maximize diversity, fostering specialization followed by
sharing of fit actions. Square (as opposed to toroidal) worlds also exhibit
higher diversity. Introducing a leader that broadcasts its actions throughout
the population increases the fitness of actions but reduces diversity; these
effects diminish the more leaders there are. Low density populations have less
fit ideas but broadcasting diminishes this effect.
Aggregate channel features for multi-view face detection
Authors: Bin Yang, Junjie Yan, Zhen Lei, Stan Z. Li
Date: 2014-07-15 00:00:00
Tasks: Face Detection, multi-view detection, Re-Ranking

Face detection has drawn much attention in recent decades since the seminal work by Viola and Jones. While many subsequences have improved the work with more powerful learning algorithms, the feature representation used for face detection still can't meet the demand for effectively and efficiently handling faces with large appearance variance in the wild. To solve this bottleneck, we borrow the concept of channel features to the face detection domain, which extends the image channel to diverse types like gradient magnitude and oriented gradient histograms and therefore encodes rich information in a simple form. We adopt a novel variant called aggregate channel features, make a full exploration of feature design, and discover a multi-scale version of features with better performance. To deal with poses of faces in the wild, we propose a multi-view detection approach featuring score re-ranking and detection adjustment. Following the learning pipelines in Viola-Jones framework, the multi-view face detector using aggregate channel features shows competitive performance against state-of-the-art algorithms on AFW and FDDB testsets, while runs at 42 FPS on VGA images.
A Truncated EM Approach for Spike-and-Slab Sparse Coding
Authors: Abdul-Saboor Sheikh, Jacquelyn A. Shelton, Jörg Lücke
Date: 2012-11-15 00:00:00
Tasks: Denoising, Image Denoising

We study inference and learning based on a sparse coding model with
`spike-and-slab' prior. As in standard sparse coding, the model used assumes
independent latent sources that linearly combine to generate data points.
However, instead of using a standard sparse prior such as a Laplace
distribution, we study the application of a more flexible `spike-and-slab'
distribution which models the absence or presence of a source's contribution
independently of its strength if it contributes. We investigate two approaches
to optimize the parameters of spike-and-slab sparse coding: a novel truncated
EM approach and, for comparison, an approach based on standard factored
variational distributions. The truncated approach can be regarded as a
variational approach with truncated posteriors as variational distributions. In
applications to source separation we find that both approaches improve the
state-of-the-art in a number of standard benchmarks, which argues for the use
of `spike-and-slab' priors for the corresponding data domains. Furthermore, we
find that the truncated EM approach improves on the standard factored approach
in source separation tasks$-$which hints to biases introduced by assuming
posterior independence in the factored variational approach. Likewise, on a
standard benchmark for image denoising, we find that the truncated EM approach
improves on the factored variational approach. While the performance of the
factored approach saturates with increasing numbers of hidden dimensions, the
performance of the truncated approach improves the state-of-the-art for higher
noise levels.
Attributes for Causal Inference in Longitudinal Observational Databases
Authors: Jenna Reps, Jonathan M. Garibaldi, Uwe Aickelin, Daniele Soria, Jack E. Gibson, Richard B. Hubbard
Date: 2014-09-03 00:00:00
Tasks: Causal Inference, feature selection, Marketing, Specificity

The pharmaceutical industry is plagued by the problem of side effects that
can occur anytime a prescribed medication is ingested. There has been a recent
interest in using the vast quantities of medical data available in longitudinal
observational databases to identify causal relationships between drugs and
medical events. Unfortunately the majority of existing post marketing
surveillance algorithms measure how dependant or associated an event is on the
presence of a drug rather than measuring causality. In this paper we
investigate potential attributes that can be used in causal inference to
identify side effects based on the Bradford-Hill causality criteria. Potential
attributes are developed by considering five of the causality criteria and
feature selection is applied to identify the most suitable of these attributes
for detecting side effects. We found that attributes based on the specificity
criterion may improve side effect signalling algorithms but the experiment and
dosage criteria attributes investigated in this paper did not offer sufficient
additional information.
Constructing a Non-Negative Low Rank and Sparse Graph with Data-Adaptive Features
Authors: Liansheng Zhuang, Shenghua Gao, Jinhui Tang, Jingjing Wang, Zhouchen Lin, Yi Ma
Date: 2014-09-03 00:00:00
Tasks: graph construction

This paper aims at constructing a good graph for discovering intrinsic data
structures in a semi-supervised learning setting. Firstly, we propose to build
a non-negative low-rank and sparse (referred to as NNLRS) graph for the given
data representation. Specifically, the weights of edges in the graph are
obtained by seeking a nonnegative low-rank and sparse matrix that represents
each data sample as a linear combination of others. The so-obtained NNLRS-graph
can capture both the global mixture of subspaces structure (by the low
rankness) and the locally linear structure (by the sparseness) of the data,
hence is both generative and discriminative. Secondly, as good features are
extremely important for constructing a good graph, we propose to learn the data
embedding matrix and construct the graph jointly within one framework, which is
termed as NNLRS with embedded features (referred to as NNLRS-EF). Extensive
experiments on three publicly available datasets demonstrate that the proposed
method outperforms the state-of-the-art graph construction method by a large
margin for both semi-supervised classification and discriminative analysis,
which verifies the effectiveness of our proposed method.
Crowd Labeling: a survey
Authors: Jafar Muhammadi, Hamid Reza Rabiee, Abbas Hosseini
Date: 2013-01-13 00:00:00
Tasks: Multiple-choice, Survey

Recently, there has been a burst in the number of research projects on human
computation via crowdsourcing. Multiple choice (or labeling) questions could be
referred to as a common type of problem which is solved by this approach. As an
application, crowd labeling is applied to find true labels for large machine
learning datasets. Since crowds are not necessarily experts, the labels they
provide are rather noisy and erroneous. This challenge is usually resolved by
collecting multiple labels for each sample, and then aggregating them to
estimate the true label. Although the mechanism leads to high-quality labels,
it is not actually cost-effective. As a result, efforts are currently made to
maximize the accuracy in estimating true labels, while fixing the number of
acquired labels.
  This paper surveys methods to aggregate redundant crowd labels in order to
estimate unknown true labels. It presents a unified statistical latent model
where the differences among popular methods in the field correspond to
different choices for the parameters of the model. Afterwards, algorithms to
make inference on these models will be surveyed. Moreover, adaptive methods
which iteratively collect labels based on the previously collected labels and
estimated models will be discussed. In addition, this paper compares the
distinguished methods, and provides guidelines for future work required to
address the current open issues.
Convolutional Neural Networks for Sentence Classification
Authors: Yoon Kim
Date: 2014-08-25 00:00:00
Proceeding: EMNLP 2014 10
Tasks: Emotion Recognition in Conversation, General Classification, Sentence, Sentence Classification, Sentiment Analysis

We report on a series of experiments with convolutional neural networks (CNN)
trained on top of pre-trained word vectors for sentence-level classification
tasks. We show that a simple CNN with little hyperparameter tuning and static
vectors achieves excellent results on multiple benchmarks. Learning
task-specific vectors through fine-tuning offers further gains in performance.
We additionally propose a simple modification to the architecture to allow for
the use of both task-specific and static vectors. The CNN models discussed
herein improve upon the state of the art on 4 out of 7 tasks, which include
sentiment analysis and question classification.
Breakdown Point of Robust Support Vector Machine
Authors: Takafumi Kanamori, Shuhei Fujiwara, Akiko Takeda
Date: 2014-09-03 00:00:00
Tasks: Outlier Detection, Sensitivity

The support vector machine (SVM) is one of the most successful learning
methods for solving classification problems. Despite its popularity, SVM has a
serious drawback, that is sensitivity to outliers in training samples. The
penalty on misclassification is defined by a convex loss called the hinge loss,
and the unboundedness of the convex loss causes the sensitivity to outliers. To
deal with outliers, robust variants of SVM have been proposed, such as the
robust outlier detection algorithm and an SVM with a bounded loss called the
ramp loss. In this paper, we propose a robust variant of SVM and investigate
its robustness in terms of the breakdown point. The breakdown point is a
robustness measure that is the largest amount of contamination such that the
estimated classifier still gives information about the non-contaminated data.
The main contribution of this paper is to show an exact evaluation of the
breakdown point for the robust SVM. For learning parameters such as the
regularization parameter in our algorithm, we derive a simple formula that
guarantees the robustness of the classifier. When the learning parameters are
determined with a grid search using cross validation, our formula works to
reduce the number of candidate search points. The robustness of the proposed
method is confirmed in numerical experiments. We show that the statistical
properties of the robust SVM are well explained by a theoretical analysis of
the breakdown point.
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
Authors: Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, Yoshua Bengio
Date: 2014-06-03 00:00:00
Tasks: Decoder, Machine Translation, Translation

In this paper, we propose a novel neural network model called RNN
Encoder-Decoder that consists of two recurrent neural networks (RNN). One RNN
encodes a sequence of symbols into a fixed-length vector representation, and
the other decodes the representation into another sequence of symbols. The
encoder and decoder of the proposed model are jointly trained to maximize the
conditional probability of a target sequence given a source sequence. The
performance of a statistical machine translation system is empirically found to
improve by using the conditional probabilities of phrase pairs computed by the
RNN Encoder-Decoder as an additional feature in the existing log-linear model.
Qualitatively, we show that the proposed model learns a semantically and
syntactically meaningful representation of linguistic phrases.
Visual Speech Recognition
Authors: Ahmad B. A. Hassanat
Date: 2014-09-03 00:00:00
Tasks: Audio-Visual Speech Recognition, Lip Reading, object-detection, Object Detection, Sign Language Recognition, Speaker Recognition, speech-recognition, Speech Recognition, Visual Speech Recognition

Lip reading is used to understand or interpret speech without hearing it, a
technique especially mastered by people with hearing difficulties. The ability
to lip read enables a person with a hearing impairment to communicate with
others and to engage in social activities, which otherwise would be difficult.
Recent advances in the fields of computer vision, pattern recognition, and
signal processing has led to a growing interest in automating this challenging
task of lip reading. Indeed, automating the human ability to lip read, a
process referred to as visual speech recognition (VSR) (or sometimes speech
reading), could open the door for other novel related applications. VSR has
received a great deal of attention in the last decade for its potential use in
applications such as human-computer interaction (HCI), audio-visual speech
recognition (AVSR), speaker recognition, talking heads, sign language
recognition and video surveillance. Its main aim is to recognise spoken word(s)
by using only the visual signal that is produced during speech. Hence, VSR
deals with the visual domain of speech and involves image processing,
artificial intelligence, object detection, pattern recognition, statistical
modelling, etc.