pwc-archive/papers-with-abstracts huggingface dataset:
Fast Kronecker product kernel methods via generalized vec trick
Authors: Antti Airola, Tapio Pahikkala
Date: 2016-01-07 00:00:00
Tasks: Collaborative Filtering, Information Retrieval, Retrieval

Kronecker product kernel provides the standard approach in the kernel methods
literature for learning from graph data, where edges are labeled and both start
and end vertices have their own feature representations. The methods allow
generalization to such new edges, whose start and end vertices do not appear in
the training data, a setting known as zero-shot or zero-data learning. Such a
setting occurs in numerous applications, including drug-target interaction
prediction, collaborative filtering and information retrieval. Efficient
training algorithms based on the so-called vec trick, that makes use of the
special structure of the Kronecker product, are known for the case where the
training data is a complete bipartite graph. In this work we generalize these
results to non-complete training graphs. This allows us to derive a general
framework for training Kronecker product kernel methods, as specific examples
we implement Kronecker ridge regression and support vector machine algorithms.
Experimental results demonstrate that the proposed approach leads to accurate
models, while allowing order of magnitude improvements in training and
prediction time.
Pattern Recognition using Artificial Immune System
Authors: Mohammad Tarek Al Muallim
Date: 2017-04-19 00:00:00
Tasks: Classification, General Classification

In this thesis, the uses of Artificial Immune Systems (AIS) in Machine
learning is studded. the thesis focus on some of immune inspired algorithms
such as clonal selection algorithm and artificial immune network. The effect of
changing the algorithm parameter on its performance is studded. Then a new
immune inspired algorithm for unsupervised classification is proposed. The new
algorithm is based on clonal selection principle and named Unsupervised Clonal
Selection Classification (UCSC). The new proposed algorithm is almost parameter
free. The algorithm parameters are data driven and it adjusts itself to make
the classification as fast as possible. The performance of UCSC is evaluated.
The experiments show that the proposed UCSC algorithm has a good performance
and more reliable.
ConvNet-Based Localization of Anatomical Structures in 3D Medical Images
Authors: Bob D. de Vos, Jelmer M. Wolterink, Pim A. de Jong, Tim Leiner, Max A. Viergever, Ivana Išgum
Date: 2017-04-19 00:00:00
Tasks: Medical Image Analysis

Localization of anatomical structures is a prerequisite for many tasks in
medical image analysis. We propose a method for automatic localization of one
or more anatomical structures in 3D medical images through detection of their
presence in 2D image slices using a convolutional neural network (ConvNet).
  A single ConvNet is trained to detect presence of the anatomical structure of
interest in axial, coronal, and sagittal slices extracted from a 3D image. To
allow the ConvNet to analyze slices of different sizes, spatial pyramid pooling
is applied. After detection, 3D bounding boxes are created by combining the
output of the ConvNet in all slices.
  In the experiments 200 chest CT, 100 cardiac CT angiography (CTA), and 100
abdomen CT scans were used. The heart, ascending aorta, aortic arch, and
descending aorta were localized in chest CT scans, the left cardiac ventricle
in cardiac CTA scans, and the liver in abdomen CT scans. Localization was
evaluated using the distances between automatically and manually defined
reference bounding box centroids and walls.
  The best results were achieved in localization of structures with clearly
defined boundaries (e.g. aortic arch) and the worst when the structure boundary
was not clearly visible (e.g. liver). The method was more robust and accurate
in localization multiple structures.
FSITM: A Feature Similarity Index For Tone-Mapped Images
Authors: Hossein Ziaei Nafchi, Atena Shahkolaei, Reza Farrahi Moghaddam, Mohamed Cheriet
Date: 2017-04-19 00:00:00
Tasks: Tone Mapping

In this work, based on the local phase information of images, an objective
index, called the feature similarity index for tone-mapped images (FSITM), is
proposed. To evaluate a tone mapping operator (TMO), the proposed index
compares the locally weighted mean phase angle map of an original high dynamic
range (HDR) to that of its associated tone-mapped image calculated using the
output of the TMO method. In experiments on two standard databases, it is shown
that the proposed FSITM method outperforms the state-of-the-art index, the tone
mapped quality index (TMQI). In addition, a higher performance is obtained by
combining the FSITM and TMQI indices. The MATLAB source code of the proposed
metric(s) is available at
https://www.mathworks.com/matlabcentral/fileexchange/59814.
Learning Linear Feature Space Transformations in Symbolic Regression
Authors: Jan Žegklitz, Petr Pošík
Date: 2017-04-17 00:00:00
Tasks: regression, Symbolic Regression

We propose a new type of leaf node for use in Symbolic Regression (SR) that
performs linear combinations of feature variables (LCF). These nodes can be
handled in three different modes -- an unsynchronized mode, where all LCFs are
free to change on their own, a synchronized mode, where LCFs are sorted into
groups in which they are forced to be identical throughout the whole
individual, and a globally synchronized mode, which is similar to the previous
mode but the grouping is done across the whole population. We also present two
methods of evolving the weights of the LCFs -- a purely stochastic way via
mutation and a gradient-based way based on the backpropagation algorithm known
from neural networks -- and also a combination of both. We experimentally
evaluate all configurations of LCFs in Multi-Gene Genetic Programming (MGGP),
which was chosen as baseline, on a number of benchmarks. According to the
results, we identified two configurations which increase the performance of the
algorithm.
Mean Deviation Similarity Index: Efficient and Reliable Full-Reference Image Quality Evaluator
Authors: Hossein Ziaei Nafchi, Atena Shahkolaei, Rachid Hedjam, Mohamed Cheriet
Date: 2016-08-26 00:00:00
Tasks: Full reference image quality assessment, Image Compression, Image Quality Assessment, Image Restoration

Applications of perceptual image quality assessment (IQA) in image and video
processing, such as image acquisition, image compression, image restoration and
multimedia communication, have led to the development of many IQA metrics. In
this paper, a reliable full reference IQA model is proposed that utilize
gradient similarity (GS), chromaticity similarity (CS), and deviation pooling
(DP). By considering the shortcomings of the commonly used GS to model human
visual system (HVS), a new GS is proposed through a fusion technique that is
more likely to follow HVS. We propose an efficient and effective formulation to
calculate the joint similarity map of two chromatic channels for the purpose of
measuring color changes. In comparison with a commonly used formulation in the
literature, the proposed CS map is shown to be more efficient and provide
comparable or better quality predictions. Motivated by a recent work that
utilizes the standard deviation pooling, a general formulation of the DP is
presented in this paper and used to compute a final score from the proposed GS
and CS maps. This proposed formulation of DP benefits from the Minkowski
pooling and a proposed power pooling as well. The experimental results on six
datasets of natural images, a synthetic dataset, and a digitally retouched
dataset show that the proposed index provides comparable or better quality
predictions than the most recent and competing state-of-the-art IQA metrics in
the literature, it is reliable and has low complexity. The MATLAB source code
of the proposed metric is available at
https://www.mathworks.com/matlabcentral/fileexchange/59809.
Dependency resolution and semantic mining using Tree Adjoining Grammars for Tamil Language
Authors: Vijay Krishna Menon, S. Rajendran, M Anandkumar, K. P. Soman
Date: 2017-04-19 00:00:00
Tasks: Sentence, TAG

Tree adjoining grammars (TAGs) provide an ample tool to capture syntax of
many Indian languages. Tamil represents a special challenge to computational
formalisms as it has extensive agglutinative morphology and a comparatively
difficult argument structure. Modelling Tamil syntax and morphology using TAG
is an interesting problem which has not been in focus even though TAGs are over
4 decades old, since its inception. Our research with Tamil TAGs have shown us
that we can not only represent syntax of the language, but to an extent mine
out semantics through dependency resolution of the sentence. But in order to
demonstrate this phenomenal property, we need to parse Tamil language sentences
using TAGs we have built and through parsing obtain a derivation we could use
to resolve dependencies, thus proving the semantic property. We use an in-house
developed pseudo lexical TAG chart parser; algorithm given by Schabes and Joshi
(1988), for generating derivations of sentences. We do not use any statistics
to rank out ambiguous derivations but rather use all of them to understand the
mentioned semantic relation with in TAGs for Tamil. We shall also present a
brief parser analysis for the completeness of our discussions.
WAHRSIS: A Low-cost, High-resolution Whole Sky Imager With Near-Infrared Capabilities
Authors: Soumyabrata Dev, Florian M. Savoy, Yee Hui Lee, Stefan Winkler
Date: 2016-05-21 00:00:00

Cloud imaging using ground-based whole sky imagers is essential for a
fine-grained understanding of the effects of cloud formations, which can be
useful in many applications. Some such imagers are available commercially, but
their cost is relatively high, and their flexibility is limited. Therefore, we
built a new daytime Whole Sky Imager (WSI) called Wide Angle High-Resolution
Sky Imaging System. The strengths of our new design are its simplicity, low
manufacturing cost and high resolution. Our imager captures the entire
hemisphere in a single high-resolution picture via a digital camera using a
fish-eye lens. The camera was modified to capture light across the visible as
well as the near-infrared spectral ranges. This paper describes the design of
the device as well as the geometric and radiometric calibration of the imaging
system.
OCRAPOSE II: An OCR-based indoor positioning system using mobile phone images
Authors: Hamed Sadeghi, Shahrokh Valaee, Shahram Shirani
Date: 2017-04-19 00:00:00
Tasks: Depth Estimation, Optical Character Recognition, Optical Character Recognition (OCR)

In this paper, we propose an OCR (optical character recognition)-based
localization system called OCRAPOSE II, which is applicable in a number of
indoor scenarios including office buildings, parkings, airports, grocery
stores, etc. In these scenarios, characters (i.e. texts or numbers) can be used
as suitable distinctive landmarks for localization. The proposed system takes
advantage of OCR to read these characters in the query still images and
provides a rough location estimate using a floor plan. Then, it finds depth and
angle-of-view of the query using the information provided by the OCR engine in
order to refine the location estimate. We derive novel formulas for the query
angle-of-view and depth estimation using image line segments and the OCR box
information. We demonstrate the applicability and effectiveness of the proposed
system through experiments in indoor scenarios. It is shown that our system
demonstrates better performance compared to the state-of-the-art benchmarks in
terms of location recognition rate and average localization error specially
under sparse database condition.
Tensor Decompositions via Two-Mode Higher-Order SVD (HOSVD)
Authors: Miaoyan Wang, Yun S. Song
Date: 2016-12-12 00:00:00
Tasks: Tensor Decomposition, Vocal Bursts Valence Prediction

Tensor decompositions have rich applications in statistics and machine
learning, and developing efficient, accurate algorithms for the problem has
received much attention recently. Here, we present a new method built on
Kruskal's uniqueness theorem to decompose symmetric, nearly orthogonally
decomposable tensors. Unlike the classical higher-order singular value
decomposition which unfolds a tensor along a single mode, we consider
unfoldings along two modes and use rank-1 constraints to characterize the
underlying components. This tensor decomposition method provably handles a
greater level of noise compared to previous methods and achieves a high
estimation accuracy. Numerical results demonstrate that our algorithm is robust
to various noise distributions and that it performs especially favorably as the
order increases.
Answering Complex Questions Using Open Information Extraction
Authors: Tushar Khot, Ashish Sabharwal, Peter Clark
Date: 2017-04-19 00:00:00
Proceeding: ACL 2017 7
Tasks: Open Information Extraction, Question Answering, Retrieval

While there has been substantial progress in factoid question-answering (QA),
answering complex questions remains challenging, typically requiring both a
large body of knowledge and inference techniques. Open Information Extraction
(Open IE) provides a way to generate semi-structured knowledge for QA, but to
date such knowledge has only been used to answer simple questions with
retrieval-based methods. We overcome this limitation by presenting a method for
reasoning with Open IE knowledge, allowing more complex questions to be
handled. Using a recently proposed support graph optimization framework for QA,
we develop a new inference model for Open IE, in particular one that can work
effectively with multiple short facts, noise, and the relational structure of
tuples. Our model significantly outperforms a state-of-the-art structured
solver on complex questions of varying difficulty, while also removing the
reliance on manually curated knowledge.
Predicting Role Relevance with Minimal Domain Expertise in a Financial Domain
Authors: Mayank Kejriwal
Date: 2017-04-19 00:00:00
Tasks: Word Embeddings

Word embeddings have made enormous inroads in recent years in a wide variety
of text mining applications. In this paper, we explore a word embedding-based
architecture for predicting the relevance of a role between two financial
entities within the context of natural language sentences. In this extended
abstract, we propose a pooled approach that uses a collection of sentences to
train word embeddings using the skip-gram word2vec architecture. We use the
word embeddings to obtain context vectors that are assigned one or more labels
based on manual annotations. We train a machine learning classifier using the
labeled context vectors, and use the trained classifier to predict contextual
role relevance on test data. Our approach serves as a good minimal-expertise
baseline for the task as it is simple and intuitive, uses open-source modules,
requires little feature crafting effort and performs well across roles.
Evolution and Analysis of Embodied Spiking Neural Networks Reveals Task-Specific Clusters of Effective Networks
Authors: Madhavun Candadai Vasu, Eduardo J. Izquierdo
Date: 2017-04-13 00:00:00
Tasks: Specificity

Elucidating principles that underlie computation in neural networks is
currently a major research topic of interest in neuroscience. Transfer Entropy
(TE) is increasingly used as a tool to bridge the gap between network
structure, function, and behavior in fMRI studies. Computational models allow
us to bridge the gap even further by directly associating individual neuron
activity with behavior. However, most computational models that have analyzed
embodied behaviors have employed non-spiking neurons. On the other hand,
computational models that employ spiking neural networks tend to be restricted
to disembodied tasks. We show for the first time the artificial evolution and
TE-analysis of embodied spiking neural networks to perform a
cognitively-interesting behavior. Specifically, we evolved an agent controlled
by an Izhikevich neural network to perform a visual categorization task. The
smallest networks capable of performing the task were found by repeating
evolutionary runs with different network sizes. Informational analysis of the
best solution revealed task-specific TE-network clusters, suggesting that
within-task homogeneity and across-task heterogeneity were key to behavioral
success. Moreover, analysis of the ensemble of solutions revealed that
task-specificity of TE-network clusters correlated with fitness. This provides
an empirically testable hypothesis that links network structure to behavior.
Using Contexts and Constraints for Improved Geotagging of Human Trafficking Webpages
Authors: Rahul Kapoor, Mayank Kejriwal, Pedro Szekely
Date: 2017-04-19 00:00:00
Tasks: Knowledge Base Construction

Extracting geographical tags from webpages is a well-motivated application in
many domains. In illicit domains with unusual language models, like human
trafficking, extracting geotags with both high precision and recall is a
challenging problem. In this paper, we describe a geotag extraction framework
in which context, constraints and the openly available Geonames knowledge base
work in tandem in an Integer Linear Programming (ILP) model to achieve good
performance. In preliminary empirical investigations, the framework improves
precision by 28.57% and F-measure by 36.9% on a difficult human trafficking
geotagging task compared to a machine learning-based baseline. The method is
already being integrated into an existing knowledge base construction system
widely used by US law enforcement agencies to combat human trafficking.
Containment for Rule-Based Ontology-Mediated Queries
Authors: Pablo Barcelo, Gerald Berger, Andreas Pieris
Date: 2017-03-23 00:00:00

Many efforts have been dedicated to identifying restrictions on ontologies
expressed as tuple-generating dependencies (tgds), a.k.a. existential rules,
that lead to the decidability for the problem of answering ontology-mediated
queries (OMQs). This has given rise to three families of formalisms: guarded,
non-recursive, and sticky sets of tgds. In this work, we study the containment
problem for OMQs expressed in such formalisms, which is a key ingredient for
solving static analysis tasks associated with them. Our main contribution is
the development of specially tailored techniques for OMQ containment under the
classes of tgds stated above. This enables us to obtain sharp complexity bounds
for the problems at hand, which in turn allow us to delimitate its practical
applicability. We also apply our techniques to pinpoint the complexity of
problems associated with two emerging applications of OMQ containment:
distribution over components and UCQ rewritability of OMQs.
Simultaneous Policy Learning and Latent State Inference for Imitating Driver Behavior
Authors: Jeremy Morton, Mykel J. Kochenderfer
Date: 2017-04-19 00:00:00

In this work, we propose a method for learning driver models that account for
variables that cannot be observed directly. When trained on a synthetic
dataset, our models are able to learn encodings for vehicle trajectories that
distinguish between four distinct classes of driver behavior. Such encodings
are learned without any knowledge of the number of driver classes or any
objective that directly requires the models to learn encodings for each class.
We show that driving policies trained with knowledge of latent variables are
more effective than baseline methods at imitating the driver behavior that they
are trained to replicate. Furthermore, we demonstrate that the actions chosen
by our policy are heavily influenced by the latent variable settings that are
provided to them.
Discovering Evolutionary Stepping Stones through Behavior Domination
Authors: Elliot Meyerson, Risto Miikkulainen
Date: 2017-04-18 00:00:00
Tasks: Diversity, Multiobjective Optimization

Behavior domination is proposed as a tool for understanding and harnessing
the power of evolutionary systems to discover and exploit useful stepping
stones. Novelty search has shown promise in overcoming deception by collecting
diverse stepping stones, and several algorithms have been proposed that combine
novelty with a more traditional fitness measure to refocus search and help
novelty search scale to more complex domains. However, combinations of novelty
and fitness do not necessarily preserve the stepping stone discovery that
novelty search affords. In several existing methods, competition between
solutions can lead to an unintended loss of diversity. Behavior domination
defines a class of algorithms that avoid this problem, while inheriting
theoretical guarantees from multiobjective optimization. Several existing
algorithms are shown to be in this class, and a new algorithm is introduced
based on fast non-dominated sorting. Experimental results show that this
algorithm outperforms existing approaches in domains that contain useful
stepping stones, and its advantage is sustained with scale. The conclusion is
that behavior domination can help illuminate the complex dynamics of
behavior-driven search, and can thus lead to the design of more scalable and
robust algorithms.
Extractive Summarization: Limits, Compression, Generalized Model and Heuristics
Authors: Rakesh Verma, Daniel Lee
Date: 2017-04-18 00:00:00
Tasks: Document Summarization, Extractive Summarization, model, Multi-Document Summarization, Text Summarization

Due to its promise to alleviate information overload, text summarization has
attracted the attention of many researchers. However, it has remained a serious
challenge. Here, we first prove empirical limits on the recall (and F1-scores)
of extractive summarizers on the DUC datasets under ROUGE evaluation for both
the single-document and multi-document summarization tasks. Next we define the
concept of compressibility of a document and present a new model of
summarization, which generalizes existing models in the literature and
integrates several dimensions of the summarization, viz., abstractive versus
extractive, single versus multi-document, and syntactic versus semantic.
Finally, we examine some new and existing single-document summarization
algorithms in a single framework and compare with state of the art summarizers
on DUC data.
Annotating Object Instances with a Polygon-RNN
Authors: Lluis Castrejon, Kaustav Kundu, Raquel Urtasun, Sanja Fidler
Date: 2017-04-18 00:00:00
Proceeding: CVPR 2017 7
Tasks: Object, Segmentation, Semantic Segmentation

We propose an approach for semi-automatic annotation of object instances.
While most current methods treat object segmentation as a pixel-labeling
problem, we here cast it as a polygon prediction task, mimicking how most
current datasets have been annotated. In particular, our approach takes as
input an image crop and sequentially produces vertices of the polygon outlining
the object. This allows a human annotator to interfere at any time and correct
a vertex if needed, producing as accurate segmentation as desired by the
annotator. We show that our approach speeds up the annotation process by a
factor of 4.7 across all classes in Cityscapes, while achieving 78.4% agreement
in IoU with original ground-truth, matching the typical agreement between human
annotators. For cars, our speed-up factor is 7.3 for an agreement of 82.2%. We
further show generalization capabilities of our approach to unseen datasets.