pwc-archive/papers-with-abstracts huggingface dataset:
Asking the metaquestions in constraint tractability
Authors: Hubie Chen, Benoit Larose
Date: 2016-04-04 00:00:00

The constraint satisfaction problem (CSP) involves deciding, given a set of
variables and a set of constraints on the variables, whether or not there is an
assignment to the variables satisfying all of the constraints. One formulation
of the CSP is as the problem of deciding, given a pair (G,H) of relational
structures, whether or not there is a homomorphism from the first structure to
the second structure. The CSP is in general NP-hard; a common way to restrict
this problem is to fix the second structure H, so that each structure H gives
rise to a problem CSP(H). The problem family CSP(H) has been studied using an
algebraic approach, which links the algorithmic and complexity properties of
each problem CSP(H) to a set of operations, the so-called polymorphisms of H.
Certain types of polymorphisms are known to imply the polynomial-time
tractability of $CSP(H)$, and others are conjectured to do so. This article
systematically studies---for various classes of polymorphisms---the
computational complexity of deciding whether or not a given structure H admits
a polymorphism from the class. Among other results, we prove the
NP-completeness of deciding a condition conjectured to characterize the
tractable problems CSP(H), as well as the NP-completeness of deciding if CSP(H)
has bounded width.
Probabilistic Multigraph Modeling for Improving the Quality of Crowdsourced Affective Data
Authors: Jianbo Ye, Jia Li, Michelle G. Newman, Reginald B. Adams, Jr., James Z. Wang
Date: 2017-01-04 00:00:00

We proposed a probabilistic approach to joint modeling of participants'
reliability and humans' regularity in crowdsourced affective studies.
Reliability measures how likely a subject will respond to a question seriously;
and regularity measures how often a human will agree with other
seriously-entered responses coming from a targeted population.
Crowdsourcing-based studies or experiments, which rely on human self-reported
affect, pose additional challenges as compared with typical crowdsourcing
studies that attempt to acquire concrete non-affective labels of objects. The
reliability of participants has been massively pursued for typical
non-affective crowdsourcing studies, whereas the regularity of humans in an
affective experiment in its own right has not been thoroughly considered. It
has been often observed that different individuals exhibit different feelings
on the same test question, which does not have a sole correct response in the
first place. High reliability of responses from one individual thus cannot
conclusively result in high consensus across individuals. Instead, globally
testing consensus of a population is of interest to investigators. Built upon
the agreement multigraph among tasks and workers, our probabilistic model
differentiates subject regularity from population reliability. We demonstrate
the method's effectiveness for in-depth robust analysis of large-scale
crowdsourced affective data, including emotion and aesthetic assessments
collected by presenting visual stimuli to human subjects.
Crime Topic Modeling
Authors: Da Kuang, P. Jeffrey Brantingham, Andrea L. Bertozzi
Date: 2017-01-05 00:00:00
Tasks: Clustering

The classification of crime into discrete categories entails a massive loss
of information. Crimes emerge out of a complex mix of behaviors and situations,
yet most of these details cannot be captured by singular crime type labels.
This information loss impacts our ability to not only understand the causes of
crime, but also how to develop optimal crime prevention strategies. We apply
machine learning methods to short narrative text descriptions accompanying
crime records with the goal of discovering ecologically more meaningful latent
crime classes. We term these latent classes "crime topics" in reference to
text-based topic modeling methods that produce them. We use topic distributions
to measure clustering among formally recognized crime types. Crime topics
replicate broad distinctions between violent and property crime, but also
reveal nuances linked to target characteristics, situational conditions and the
tools and methods of attack. Formal crime types are not discrete in topic
space. Rather, crime types are distributed across a range of crime topics.
Similarly, individual crime topics are distributed across a range of formal
crime types. Key ecological groups include identity theft, shoplifting,
burglary and theft, car crimes and vandalism, criminal threats and confidence
crimes, and violent crimes. Though not a replacement for formal legal crime
classifications, crime topics provide a unique window into the heterogeneous
causal processes underlying crime.
Learning local trajectories for high precision robotic tasks : application to KUKA LBR iiwa Cartesian positioning
Authors: Joris Guerin, Olivier Gibaru, Eric Nyiri, Stephane Thiery
Date: 2017-01-05 00:00:00
Tasks: regression

To ease the development of robot learning in industry, two conditions need to
be fulfilled. Manipulators must be able to learn high accuracy and precision
tasks while being safe for workers in the factory. In this paper, we extend
previously submitted work which consists in rapid learning of local high
accuracy behaviors. By exploration and regression, linear and quadratic models
are learnt for respectively the dynamics and cost function. Iterative Linear
Quadratic Gaussian Regulator combined with cost quadratic regression can
converge rapidly in the final stages towards high accuracy behavior as the cost
function is modelled quite precisely. In this paper, both a different cost
function and a second order improvement method are implemented within this
framework. We also propose an analysis of the algorithm parameters through
simulation for a positioning task. Finally, an experimental validation on a
KUKA LBR iiwa robot is carried out. This collaborative robot manipulator can be
easily programmed into safety mode, which makes it qualified for the second
industry constraint stated above.
Membrane-Dependent Neuromorphic Learning Rule for Unsupervised Spike Pattern Detection
Authors: Sadique Sheik, Somnath Paul, Charles Augustine, Gert Cauwenberghs
Date: 2017-01-05 00:00:00

Several learning rules for synaptic plasticity, that depend on either spike
timing or internal state variables, have been proposed in the past imparting
varying computational capabilities to Spiking Neural Networks. Due to design
complications these learning rules are typically not implemented on
neuromorphic devices leaving the devices to be only capable of inference. In
this work we propose a unidirectional post-synaptic potential dependent
learning rule that is only triggered by pre-synaptic spikes, and easy to
implement on hardware. We demonstrate that such a learning rule is functionally
capable of replicating computational capabilities of pairwise STDP. Further
more, we demonstrate that this learning rule can be used to learn and classify
spatio-temporal spike patterns in an unsupervised manner using individual
neurons. We argue that this learning rule is computationally powerful and also
ideal for hardware implementations due to its unidirectional memory access.
Designing a Safe Autonomous Artificial Intelligence Agent based on Human Self-Regulation
Authors: Mark Muraven
Date: 2017-01-05 00:00:00
Tasks: AI Agent

There is a growing focus on how to design safe artificial intelligent (AI)
agents. As systems become more complex, poorly specified goals or control
mechanisms may cause AI agents to engage in unwanted and harmful outcomes. Thus
it is necessary to design AI agents that follow initial programming intentions
as the program grows in complexity. How to specify these initial intentions has
also been an obstacle to designing safe AI agents. Finally, there is a need for
the AI agent to have redundant safety mechanisms to ensure that any programming
errors do not cascade into major problems. Humans are autonomous intelligent
agents that have avoided these problems and the present manuscript argues that
by understanding human self-regulation and goal setting, we may be better able
to design safe AI agents. Some general principles of human self-regulation are
outlined and specific guidance for AI design is given.
Identifying Designs from Incomplete, Fragmented Cultural Heritage Objects by Curve-Pattern Matching
Authors: Jun Zhou, Haozhou Yu, Karen Smith, Colin Wilder, Hongkai Yu, Song Wang
Date: 2016-08-05 00:00:00
Tasks: Management, Object

Study of cultural-heritage objects with embellished realistic and abstract
designs made up of connected and intertwined curves crosscuts a number of
related disciplines, including archaeology, art history, and heritage
management. However, many objects, such as pottery sherds found in the
archaeological record, are fragmentary, making the underlying complete designs
unknowable at the scale of the sherd fragment. The challenge to reconstruct and
study complete designs is stymied because 1) most fragmentary cultural-heritage
objects contain only a small portion of the underlying full design, 2) in the
case of a stamping application, the same design may be applied multiple times
with spatial overlap on one object, and 3) curve patterns detected on an object
are usually incomplete and noisy. As a result, classical curve-pattern matching
algorithms, such as Chamfer matching, may perform poorly in identifying the
underlying design. In this paper, we develop a new partial-to-global curve
matching algorithm to address these challenges and better identify the full
design from a fragmented cultural heritage object. Specifically, we develop the
algorithm to identify the designs of the carved wooden paddles of the
Southeastern Woodlands from unearthed pottery sherds. A set of pottery sherds
from the Snow Collection, curated at Georgia Southern University, are used to
test the proposed algorithm, with promising results.
Quantitative Analysis of Automatic Image Cropping Algorithms: A Dataset and Comparative Study
Authors: Yi-Ling Chen, Tzu-Wei Huang, Kai-Han Chang, Yu-Chen Tsai, Hwann-Tzong Chen, Bing-Yu Chen
Date: 2017-01-05 00:00:00
Tasks: Image Cropping, Learning-To-Rank, Saliency Detection

Automatic photo cropping is an important tool for improving visual quality of
digital photos without resorting to tedious manual selection. Traditionally,
photo cropping is accomplished by determining the best proposal window through
visual quality assessment or saliency detection. In essence, the performance of
an image cropper highly depends on the ability to correctly rank a number of
visually similar proposal windows. Despite the ranking nature of automatic
photo cropping, little attention has been paid to learning-to-rank algorithms
in tackling such a problem. In this work, we conduct an extensive study on
traditional approaches as well as ranking-based croppers trained on various
image features. In addition, a new dataset consisting of high quality cropping
and pairwise ranking annotations is presented to evaluate the performance of
various baselines. The experimental results on the new dataset provide useful
insights into the design of better photo cropping algorithms.
Graph Structure Learning from Unlabeled Data for Event Detection
Authors: Sriram Somanchi, Daniel B. Neill
Date: 2017-01-05 00:00:00
Tasks: Event Detection, Graph structure learning

Processes such as disease propagation and information diffusion often spread
over some latent network structure which must be learned from observation.
Given a set of unlabeled training examples representing occurrences of an event
type of interest (e.g., a disease outbreak), our goal is to learn a graph
structure that can be used to accurately detect future events of that type.
Motivated by new theoretical results on the consistency of constrained and
unconstrained subset scans, we propose a novel framework for learning graph
structure from unlabeled data by comparing the most anomalous subsets detected
with and without the graph constraints. Our framework uses the mean normalized
log-likelihood ratio score to measure the quality of a graph structure, and
efficiently searches for the highest-scoring graph structure. Using simulated
disease outbreaks injected into real-world Emergency Department data from
Allegheny County, we show that our method learns a structure similar to the
true underlying graph, but enables faster and more accurate detection.
Understanding the complexity of #SAT using knowledge compilation
Authors: Florent Capelli
Date: 2017-01-05 00:00:00

Two main techniques have been used so far to solve the #P-hard problem #SAT.
The first one, used in practice, is based on an extension of DPLL for model
counting called exhaustive DPLL. The second approach, more theoretical,
exploits the structure of the input to compute the number of satisfying
assignments by usually using a dynamic programming scheme on a decomposition of
the formula. In this paper, we make a first step toward the separation of these
two techniques by exhibiting a family of formulas that can be solved in
polynomial time with the first technique but needs an exponential time with the
second one. We show this by observing that both techniques implicitely
construct a very specific boolean circuit equivalent to the input formula. We
then show that every beta-acyclic formula can be represented by a polynomial
size circuit corresponding to the first method and exhibit a family of
beta-acyclic formulas which cannot be represented by polynomial size circuits
corresponding to the second method. This result shed a new light on the
complexity of #SAT and related problems on beta-acyclic formulas. As a
byproduct, we give new handy tools to design algorithms on beta-acyclic
hypergraphs.
Exploration of Proximity Heuristics in Length Normalization
Authors: Pranav Agrawal
Date: 2017-01-05 00:00:00
Tasks: Feature Engineering, Information Retrieval, Retrieval

Ranking functions used in information retrieval are primarily used in the
search engines and they are often adopted for various language processing
applications. However, features used in the construction of ranking functions
should be analyzed before applying it on a data set. This paper gives
guidelines on construction of generalized ranking functions with
application-dependent features. The paper prescribes a specific case of a
generalized function for recommendation system using feature engineering
guidelines on the given data set. The behavior of both generalized and specific
functions are studied and implemented on the unstructured textual data. The
proximity feature based ranking function has outperformed by 52% from regular
BM25.
Facial Expression Recognition from World Wild Web
Authors: Ali Mollahosseini, Behzad Hassani, Michelle J. Salvador, Hojjat Abdollahi, David Chan, Mohammad H. Mahoor
Date: 2016-05-11 00:00:00
Tasks: Facial Expression Recognition, Facial Expression Recognition (FER)

Recognizing facial expression in a wild setting has remained a challenging
task in computer vision. The World Wide Web is a good source of facial images
which most of them are captured in uncontrolled conditions. In fact, the
Internet is a Word Wild Web of facial images with expressions. This paper
presents the results of a new study on collecting, annotating, and analyzing
wild facial expressions from the web. Three search engines were queried using
1250 emotion related keywords in six different languages and the retrieved
images were mapped by two annotators to six basic expressions and neutral. Deep
neural networks and noise modeling were used in three different training
scenarios to find how accurately facial expressions can be recognized when
trained on noisy images collected from the web using query terms (e.g. happy
face, laughing man, etc)? The results of our experiments show that deep neural
networks can recognize wild facial expressions with an accuracy of 82.12%.
Applications of Algorithmic Probability to the Philosophy of Mind
Authors: Gabriel Leuenberger
Date: 2014-04-07 00:00:00
Tasks: Ethics, Philosophy

This paper presents formulae that can solve various seemingly hopeless
philosophical conundrums. We discuss the simulation argument, teleportation,
mind-uploading, the rationality of utilitarianism, and the ethics of exploiting
artificial general intelligence. Our approach arises from combining the
essential ideas of formalisms such as algorithmic probability, the universal
intelligence measure, space-time-embedded intelligence, and Hutter's observer
localization. We argue that such universal models can yield the ultimate
solutions, but a novel research direction would be required in order to find
computationally efficient approximations thereof.
Neural Semantic Encoders
Authors: Tsendsuren Munkhdalai, Hong Yu
Date: 2016-07-14 00:00:00
Proceeding: EACL 2017 4
Tasks: General Classification, Machine Translation, Natural Language Inference, Natural Language Understanding, Question Answering, Sentence, Sentence Classification, Sentiment Analysis, Translation

We present a memory augmented neural network for natural language
understanding: Neural Semantic Encoders. NSE is equipped with a novel memory
update rule and has a variable sized encoding memory that evolves over time and
maintains the understanding of input sequences through read}, compose and write
operations. NSE can also access multiple and shared memories. In this paper, we
demonstrated the effectiveness and the flexibility of NSE on five different
natural language tasks: natural language inference, question answering,
sentence classification, document sentiment analysis and machine translation
where NSE achieved state-of-the-art performance when evaluated on publically
available benchmarks. For example, our shared-memory model showed an
encouraging result on neural machine translation, improving an attention-based
baseline by approximately 1.0 BLEU.
NeuroRule: A Connectionist Approach to Data Mining
Authors: Hongjun Lu, Rudy Setiono, Huan Liu
Date: 2017-01-05 00:00:00
Tasks: General Classification

Classification, which involves finding rules that partition a given data set
into disjoint groups, is one class of data mining problems. Approaches proposed
so far for mining classification rules for large databases are mainly decision
tree based symbolic learning methods. The connectionist approach based on
neural networks has been thought not well suited for data mining. One of the
major reasons cited is that knowledge generated by neural networks is not
explicitly represented in the form of rules suitable for verification or
interpretation by humans. This paper examines this issue. With our newly
developed algorithms, rules which are similar to, or more concise than those
generated by the symbolic methods can be extracted from the neural networks.
The data mining process using neural networks with the emphasis on rule
extraction is described. Experimental results and comparison with previously
published works are presented.
Gaussian Process Quadrature Moment Transform
Authors: Jakub Prüher, Ondřej Straka
Date: 2017-01-05 00:00:00

Computation of moments of transformed random variables is a problem appearing
in many engineering applications. The current methods for moment transformation
are mostly based on the classical quadrature rules which cannot account for the
approximation errors. Our aim is to design a method for moment transformation
for Gaussian random variables which accounts for the error in the numerically
computed mean. We employ an instance of Bayesian quadrature, called Gaussian
process quadrature (GPQ), which allows us to treat the integral itself as a
random variable, where the integral variance informs about the incurred
integration error. Experiments on the coordinate transformation and nonlinear
filtering examples show that the proposed GPQ moment transform performs better
than the classical transforms.
Generating Focussed Molecule Libraries for Drug Discovery with Recurrent Neural Networks
Authors: Marwin H. S. Segler, Thierry Kogej, Christian Tyrchan, Mark P. Waller
Date: 2017-01-05 00:00:00
Tasks: Drug Design, Drug Discovery

In de novo drug design, computational strategies are used to generate novel
molecules with good affinity to the desired biological target. In this work, we
show that recurrent neural networks can be trained as generative models for
molecular structures, similar to statistical language models in natural
language processing. We demonstrate that the properties of the generated
molecules correlate very well with the properties of the molecules used to
train the model. In order to enrich libraries with molecules active towards a
given biological target, we propose to fine-tune the model with small sets of
molecules, which are known to be active against that target.
  Against Staphylococcus aureus, the model reproduced 14% of 6051 hold-out test
molecules that medicinal chemists designed, whereas against Plasmodium
falciparum (Malaria) it reproduced 28% of 1240 test molecules. When coupled
with a scoring function, our model can perform the complete de novo drug design
cycle to generate large sets of novel molecules for drug discovery.
Outlier Detection for Text Data : An Extended Version
Authors: Ramakrishnan Kannan, Hyenkyun Woo, Charu C. Aggarwal, Haesun Park
Date: 2017-01-05 00:00:00
Tasks: Attribute, Outlier Detection

The problem of outlier detection is extremely challenging in many domains
such as text, in which the attribute values are typically non-negative, and
most values are zero. In such cases, it often becomes difficult to separate the
outliers from the natural variations in the patterns in the underlying data. In
this paper, we present a matrix factorization method, which is naturally able
to distinguish the anomalies with the use of low rank approximations of the
underlying data. Our iterative algorithm TONMF is based on block coordinate
descent (BCD) framework. We define blocks over the term-document matrix such
that the function becomes solvable. Given most recently updated values of other
matrix blocks, we always update one block at a time to its optimal. Our
approach has significant advantages over traditional methods for text outlier
detection. Finally, we present experimental results illustrating the
effectiveness of our method over competing methods.
Free-Space Detection with Self-Supervised and Online Trained Fully Convolutional Networks
Authors: Willem P. Sanberg, Gijs Dubbelman, Peter H. N. de With
Date: 2016-04-08 00:00:00

Recently, vision-based Advanced Driver Assist Systems have gained broad
interest. In this work, we investigate free-space detection, for which we
propose to employ a Fully Convolutional Network (FCN). We show that this FCN
can be trained in a self-supervised manner and achieve similar results compared
to training on manually annotated data, thereby reducing the need for large
manually annotated training sets. To this end, our self-supervised training
relies on a stereo-vision disparity system, to automatically generate (weak)
training labels for the color-based FCN. Additionally, our self-supervised
training facilitates online training of the FCN instead of offline.
Consequently, given that the applied FCN is relatively small, the free-space
analysis becomes highly adaptive to any traffic scene that the vehicle
encounters. We have validated our algorithm using publicly available data and
on a new challenging benchmark dataset that is released with this paper.
Experiments show that the online training boosts performance with 5% when
compared to offline training, both for Fmax and AP.