pwc-archive/papers-with-abstracts huggingface dataset:
claspfolio 2: Advances in Algorithm Selection for Answer Set Programming
Authors: Holger Hoos, Marius Lindauer, Torsten Schaub
Date: 2014-05-07 00:00:00

To appear in Theory and Practice of Logic Programming (TPLP). Building on the
award-winning, portfolio-based ASP solver claspfolio, we present claspfolio 2,
a modular and open solver architecture that integrates several different
portfolio-based algorithm selection approaches and techniques. The claspfolio 2
solver framework supports various feature generators, solver selection
approaches, solver portfolios, as well as solver-schedule-based pre-solving
techniques. The default configuration of claspfolio 2 relies on a light-weight
version of the ASP solver clasp to generate static and dynamic instance
features. The flexible open design of claspfolio 2 is a distinguishing factor
even beyond ASP. As such, it provides a unique framework for comparing and
combining existing portfolio-based algorithm selection approaches and
techniques in a single, unified framework. Taking advantage of this, we
conducted an extensive experimental study to assess the impact of different
feature sets, selection approaches and base solver portfolios. In addition to
gaining substantial insights into the utility of the various approaches and
techniques, we identified a default configuration of claspfolio 2 that achieves
substantial performance gains not only over clasp's default configuration and
the earlier version of claspfolio 2, but also over manually tuned
configurations of clasp.
A Mathematical Theory of Learning
Authors: Ibrahim Alabdulmohsin
Date: 2014-05-07 00:00:00
Tasks: Clustering, Learning Theory

In this paper, a mathematical theory of learning is proposed that has many
parallels with information theory. We consider Vapnik's General Setting of
Learning in which the learning process is defined to be the act of selecting a
hypothesis in response to a given training set. Such hypothesis can, for
example, be a decision boundary in classification, a set of centroids in
clustering, or a set of frequent item-sets in association rule mining.
Depending on the hypothesis space and how the final hypothesis is selected, we
show that a learning process can be assigned a numeric score, called learning
capacity, which is analogous to Shannon's channel capacity and satisfies
similar interesting properties as well such as the data-processing inequality
and the information-cannot-hurt inequality. In addition, learning capacity
provides the tightest possible bound on the difference between true risk and
empirical risk of the learning process for all loss functions that are
parametrized by the chosen hypothesis. It is also shown that the notion of
learning capacity equivalently quantifies how sensitive the choice of the final
hypothesis is to a small perturbation in the training set. Consequently,
algorithmic stability is both necessary and sufficient for generalization.
While the theory does not rely on concentration inequalities, we finally show
that analogs to classical results in learning theory using the Probably
Approximately Correct (PAC) model can be immediately deduced using this theory,
and conclude with information-theoretic bounds to learning capacity.
Pulling back error to the hidden-node parameter technology: Single-hidden-layer feedforward network without output weight
Authors: Yimin Yang, Q. M. Jonathan Wu, Guang-Bin Huang, Yaonan Wang
Date: 2014-05-06 00:00:00

According to conventional neural network theories, the feature of
single-hidden-layer feedforward neural networks(SLFNs) resorts to parameters of
the weighted connections and hidden nodes. SLFNs are universal approximators
when at least the parameters of the networks including hidden-node parameter
and output weight are exist. Unlike above neural network theories, this paper
indicates that in order to let SLFNs work as universal approximators, one may
simply calculate the hidden node parameter only and the output weight is not
needed at all. In other words, this proposed neural network architecture can be
considered as a standard SLFNs with fixing output weight equal to an unit
vector. Further more, this paper presents experiments which show that the
proposed learning method tends to extremely reduce network output error to a
very small number with only 1 hidden node. Simulation results demonstrate that
the proposed method can provide several to thousands of times faster than other
learning algorithm including BP, SVM/SVR and other ELM methods.
Understanding Protein Dynamics with L1-Regularized Reversible Hidden Markov Models
Authors: Robert T. McGibbon, Bharath Ramsundar, Mohammad M. Sultan, Gert Kiss, Vijay S. Pande
Date: 2014-05-06 00:00:00
Tasks: Distributed Computing, Drug Design, Model Selection

We present a machine learning framework for modeling protein dynamics. Our
approach uses L1-regularized, reversible hidden Markov models to understand
large protein datasets generated via molecular dynamics simulations. Our model
is motivated by three design principles: (1) the requirement of massive
scalability; (2) the need to adhere to relevant physical law; and (3) the
necessity of providing accessible interpretations, critical for both cellular
biology and rational drug design. We present an EM algorithm for learning and
introduce a model selection criteria based on the physical notion of
convergence in relaxation timescales. We contrast our model with standard
methods in biophysics and demonstrate improved robustness. We implement our
algorithm on GPUs and apply the method to two large protein simulation datasets
generated respectively on the NCSA Bluewaters supercomputer and the
Folding@Home distributed computing network. Our analysis identifies the
conformational dynamics of the ubiquitin protein critical to cellular
signaling, and elucidates the stepwise activation mechanism of the c-Src kinase
protein.
The effect of wording on message propagation: Topic- and author-controlled natural experiments on Twitter
Authors: Chenhao Tan, Lillian Lee, Bo Pang
Date: 2014-05-06 00:00:00
Proceeding: ACL 2014 6

Consider a person trying to spread an important message on a social network.
He/she can spend hours trying to craft the message. Does it actually matter?
While there has been extensive prior work looking into predicting popularity of
social-media content, the effect of wording per se has rarely been studied
since it is often confounded with the popularity of the author and the topic.
To control for these confounding factors, we take advantage of the surprising
fact that there are many pairs of tweets containing the same url and written by
the same user but employing different wording. Given such pairs, we ask: which
version attracts more retweets? This turns out to be a more difficult task than
predicting popular topics. Still, humans can answer this question better than
chance (but far from perfectly), and the computational methods we develop can
do better than both an average human and a strong competing method trained on
non-controlled data.
Training Restricted Boltzmann Machine by Perturbation
Authors: Siamak Ravanbakhsh, Russell Greiner, Brendan Frey
Date: 2014-05-06 00:00:00

A new approach to maximum likelihood learning of discrete graphical models
and RBM in particular is introduced. Our method, Perturb and Descend (PD) is
inspired by two ideas (I) perturb and MAP method for sampling (II) learning by
Contrastive Divergence minimization. In contrast to perturb and MAP, PD
leverages training data to learn the models that do not allow efficient MAP
estimation. During the learning, to produce a sample from the current model, we
start from a training data and descend in the energy landscape of the
"perturbed model", for a fixed number of steps, or until a local optima is
reached. For RBM, this involves linear calculations and thresholding which can
be very fast. Furthermore we show that the amount of perturbation is closely
related to the temperature parameter and it can regularize the model by
producing robust features resulting in sparse hidden layer activation.
How Community Feedback Shapes User Behavior
Authors: Justin Cheng, Cristian Danescu-Niculescu-Mizil, Jure Leskovec
Date: 2014-05-06 00:00:00

Social media systems rely on user feedback and rating mechanisms for
personalization, ranking, and content filtering. However, when users evaluate
content contributed by fellow users (e.g., by liking a post or voting on a
comment), these evaluations create complex social feedback effects. This paper
investigates how ratings on a piece of content affect its author's future
behavior. By studying four large comment-based news communities, we find that
negative feedback leads to significant behavioral changes that are detrimental
to the community. Not only do authors of negatively-evaluated content
contribute more, but also their future posts are of lower quality, and are
perceived by the community as such. Moreover, these authors are more likely to
subsequently evaluate their fellow users negatively, percolating these effects
through the community. In contrast, positive feedback does not carry similar
effects, and neither encourages rewarded authors to write more, nor improves
the quality of their posts. Interestingly, the authors that receive no feedback
are most likely to leave a community. Furthermore, a structural analysis of the
voter network reveals that evaluations polarize the community the most when
positive and negative votes are equally split.
D-Bees: A Novel Method Inspired by Bee Colony Optimization for Solving Word Sense Disambiguation
Authors: Sallam Abualhaija, Karl-Heinz Zimmermann
Date: 2014-05-06 00:00:00
Tasks: Combinatorial Optimization, Word Sense Disambiguation

Word sense disambiguation (WSD) is a problem in the field of computational
linguistics given as finding the intended sense of a word (or a set of words)
when it is activated within a certain context. WSD was recently addressed as a
combinatorial optimization problem in which the goal is to find a sequence of
senses that maximize the semantic relatedness among the target words. In this
article, a novel algorithm for solving the WSD problem called D-Bees is
proposed which is inspired by bee colony optimization (BCO)where artificial bee
agents collaborate to solve the problem. The D-Bees algorithm is evaluated on a
standard dataset (SemEval 2007 coarse-grained English all-words task corpus)and
is compared to simulated annealing, genetic algorithms, and two ant colony
optimization techniques (ACO). It will be observed that the BCO and ACO
approaches are on par.
Exemplar Dynamics Models of the Stability of Phonological Categories
Authors: P. F. Tupper
Date: 2014-04-30 00:00:00

We develop a model for the stability and maintenance of phonological
categories. Examples of phonological categories are vowel sounds such as "i"
and "e". We model such categories as consisting of collections of labeled
exemplars that language users store in their memory. Each exemplar is a
detailed memory of an instance of the linguistic entity in question. Starting
from an exemplar-level model we derive integro-differential equations for the
long-term evolution of the density of exemplars in different portions of
phonetic space. Using these latter equations we investigate under what
conditions two phonological categories merge or not. Our main conclusion is
that for the preservation of distinct phonological categories, it is necessary
that anomalous speech tokens of a given category are discarded, and not merely
stored in memory as an exemplar of another category.
Automatic Method Of Domain Ontology Construction based on Characteristics of Corpora POS-Analysis
Authors: Olena Orobinska
Date: 2014-05-06 00:00:00
Tasks: POS, text annotation

It is now widely recognized that ontologies, are one of the fundamental
cornerstones of knowledge-based systems. What is lacking, however, is a
currently accepted strategy of how to build ontology; what kinds of the
resources and techniques are indispensables to optimize the expenses and the
time on the one hand and the amplitude, the completeness, the robustness of en
ontology on the other hand. The paper offers a semi-automatic ontology
construction method from text corpora in the domain of radiological protection.
This method is composed from next steps: 1) text annotation with part-of-speech
tags; 2) revelation of the significant linguistic structures and forming the
templates; 3) search of text fragments corresponding to these templates; 4)
basic ontology instantiation process
Semantics and Compilation of Answer Set Programming with Generalized Atoms
Authors: Mario Alviano, Wolfgang Faber
Date: 2014-05-06 00:00:00

Answer Set Programming (ASP) is logic programming under the stable model or
answer set semantics. During the last decade, this paradigm has seen several
extensions by generalizing the notion of atom used in these programs. Among
these, there are aggregate atoms, HEX atoms, generalized quantifiers, and
abstract constraints. In this paper we refer to these constructs collectively
as generalized atoms. The idea common to all of these constructs is that their
satisfaction depends on the truth values of a set of (non-generalized) atoms,
rather than the truth value of a single (non-generalized) atom. Motivated by
several examples, we argue that for some of the more intricate generalized
atoms, the previously suggested semantics provide unintuitive results and
provide an alternative semantics, which we call supportedly stable or SFLP
answer sets. We show that it is equivalent to the major previously proposed
semantics for programs with convex generalized atoms, and that it in general
admits more intended models than other semantics in the presence of non-convex
generalized atoms. We show that the complexity of supportedly stable models is
on the second level of the polynomial hierarchy, similar to previous proposals
and to stable models of disjunctive logic programs. Given these complexity
results, we provide a compilation method that compactly transforms programs
with generalized atoms in disjunctive normal form to programs without
generalized atoms. Variants are given for the new supportedly stable and the
existing FLP semantics, for which a similar compilation technique has not been
known so far.
Nuclear Norm based Matrix Regression with Applications to Face Recognition with Occlusion and Illumination Changes
Authors: Jian Yang, Jianjun Qian, Lei Luo, Fanlong Zhang, Yicheng Gao
Date: 2014-05-06 00:00:00
Tasks: Face Recognition, regression

Recently regression analysis becomes a popular tool for face recognition. The existing regression methods all use the one-dimensional pixel-based error model, which characterizes the representation error pixel by pixel individually and thus neglects the whole structure of the error image. We observe that occlusion and illumination changes generally lead to a low-rank error image. To make use of this low-rank structural information, this paper presents a two-dimensional image matrix based error model, i.e. matrix regression, for face representation and classification. Our model uses the minimal nuclear norm of representation error image as a criterion, and the alternating direction method of multipliers method to calculate the regression coefficients. Compared with the current regression methods, the proposed Nuclear Norm based Matrix Regression (NMR) model is more robust for alleviating the effect of illumination, and more intuitive and powerful for removing the structural noise caused by occlusion. We experiment using four popular face image databases, the Extended Yale B database, the AR database, the Multi-PIE and the FRGC database. Experimental results demonstrate the performance advantage of NMR over the state-of-the-art regression based face recognition methods.
Some thoughts about benchmarks for NMR
Authors: Daniel Le Berre
Date: 2014-05-06 00:00:00

The NMR community would like to build a repository of benchmarks to push
forward the design of systems implementing NMR as it has been the case for many
other areas in AI. There are a number of lessons which can be learned from the
experience of other communi- ties. Here are a few thoughts about the
requirements and choices to make before building such a repository.
An ASP-Based Architecture for Autonomous UAVs in Dynamic Environments: Progress Report
Authors: Marcello Balduccini, William C. Regli, Duc N. Nguyen
Date: 2014-05-06 00:00:00
Tasks: Scheduling

Traditional AI reasoning techniques have been used successfully in many
domains, including logistics, scheduling and game playing. This paper is part
of a project aimed at investigating how such techniques can be extended to
coordinate teams of unmanned aerial vehicles (UAVs) in dynamic environments.
Specifically challenging are real-world environments where UAVs and other
network-enabled devices must communicate to coordinate---and communication
actions are neither reliable nor free. Such network-centric environments are
common in military, public safety and commercial applications, yet most
research (even multi-agent planning) usually takes communications among
distributed agents as a given. We address this challenge by developing an agent
architecture and reasoning algorithms based on Answer Set Programming (ASP).
ASP has been chosen for this task because it enables high flexibility of
representation, both of knowledge and of reasoning tasks. Although ASP has been
used successfully in a number of applications, and ASP-based architectures have
been studied for about a decade, to the best of our knowledge this is the first
practical application of a complete ASP-based agent architecture. It is also
the first practical application of ASP involving a combination of centralized
reasoning, decentralized reasoning, execution monitoring, and reasoning about
network communications. This work has been empirically validated using a
distributed network-centric software evaluation testbed and the results provide
guidance to designers in how to understand and control intelligent systems that
operate in these environments.
PANDA: Pose Aligned Networks for Deep Attribute Modeling
Authors: Ning Zhang, Manohar Paluri, Marc'Aurelio Ranzato, Trevor Darrell, Lubomir Bourdev
Date: 2013-11-21 00:00:00
Proceeding: CVPR 2014 6
Tasks: Attribute, Facial Attribute Classification, General Classification, Object Recognition

We propose a method for inferring human attributes (such as gender, hair
style, clothes style, expression, action) from images of people under large
variation of viewpoint, pose, appearance, articulation and occlusion.
Convolutional Neural Nets (CNN) have been shown to perform very well on large
scale object recognition problems. In the context of attribute classification,
however, the signal is often subtle and it may cover only a small part of the
image, while the image is dominated by the effects of pose and viewpoint.
Discounting for pose variation would require training on very large labeled
datasets which are not presently available. Part-based models, such as poselets
and DPM have been shown to perform well for this problem but they are limited
by shallow low-level features. We propose a new method which combines
part-based models and deep learning by training pose-normalized CNNs. We show
substantial improvement vs. state-of-the-art methods on challenging attribute
classification tasks in unconstrained settings. Experiments confirm that our
method outperforms both the best part-based methods on this problem and
conventional CNNs trained on the full bounding box of the person.
KR$^3$: An Architecture for Knowledge Representation and Reasoning in Robotics
Authors: Shiqi Zhang, Mohan Sridharan, Michael Gelfond, Jeremy Wyatt
Date: 2014-05-05 00:00:00

This paper describes an architecture that combines the complementary
strengths of declarative programming and probabilistic graphical models to
enable robots to represent, reason with, and learn from, qualitative and
quantitative descriptions of uncertainty and knowledge. An action language is
used for the low-level (LL) and high-level (HL) system descriptions in the
architecture, and the definition of recorded histories in the HL is expanded to
allow prioritized defaults. For any given goal, tentative plans created in the
HL using default knowledge and commonsense reasoning are implemented in the LL
using probabilistic algorithms, with the corresponding observations used to
update the HL history. Tight coupling between the two levels enables automatic
selection of relevant variables and generation of suitable action policies in
the LL for each HL action, and supports reasoning with violation of defaults,
noisy observations and unreliable actions in large and complex domains. The
architecture is evaluated in simulation and on physical robots transporting
objects in indoor domains; the benefit on robots is a reduction in task
execution time of 39% compared with a purely probabilistic, but still
hierarchical, approach.
Learning Bilingual Word Representations by Marginalizing Alignments
Authors: Tomáš Kočiský, Karl Moritz Hermann, Phil Blunsom
Date: 2014-05-05 00:00:00
Proceeding: ACL 2014 6
Tasks: General Classification

We present a probabilistic model that simultaneously learns alignments and
distributed representations for bilingual data. By marginalizing over word
alignments the model captures a larger semantic context than prior work relying
on hard alignments. The advantage of this approach is demonstrated in a
cross-lingual classification task, where we outperform the prior published
state of the art.
Towards a Benchmark of Natural Language Arguments
Authors: Elena Cabrio, Serena Villata
Date: 2014-05-05 00:00:00
Tasks: Abstract Argumentation, Natural Language Inference

The connections among natural language processing and argumentation theory
are becoming stronger in the latest years, with a growing amount of works going
in this direction, in different scenarios and applying heterogeneous
techniques. In this paper, we present two datasets we built to cope with the
combination of the Textual Entailment framework and bipolar abstract
argumentation. In our approach, such datasets are used to automatically
identify through a Textual Entailment system the relations among the arguments
(i.e., attack, support), and then the resulting bipolar argumentation graphs
are analyzed to compute the accepted arguments.
Design Of Fuzzy Logic Traffic Controller For Isolated Intersections With Emergency Vehicle Priority System Using MATLAB Simulation
Authors: Mohit Jha, Shailja Shukla
Date: 2014-05-05 00:00:00

Traffic is the chief puzzle problem which every country faces because of the
enhancement in number of vehicles throughout the world, especially in large
urban towns. Hence the need arises for simulating and optimizing traffic
control algorithms to better accommodate this increasing demand. Fuzzy
optimization deals with finding the values of input parameters of a complex
simulated system which result in desired output. This paper presents a MATLAB
simulation of fuzzy logic traffic controller for controlling flow of traffic in
isolated intersections. This controller is based on the waiting time and queue
length of vehicles at present green phase and vehicles queue lengths at the
other phases. The controller controls the traffic light timings and phase
difference to ascertain sebaceous flow of traffic with least waiting time and
queue length. In this paper, the isolated intersection model used consists of
two alleyways in each approach. Every outlook has different value of queue
length and waiting time, systematically, at the intersection. The maximum value
of waiting time and vehicle queue length has to be selected by using proximity
sensors as inputs to controller for the ameliorate control traffic flow at the
intersection. An intelligent traffic model and fuzzy logic traffic controller
are developed to evaluate the performance of traffic controller under different
pre-defined conditions for oleaginous flow of traffic. Additionally, this fuzzy
logic traffic controller has emergency vehicle siren sensors which detect
emergency vehicle movement like ambulance, fire brigade, Police Van etc. and
gives maximum priority to him and pass preferred signal to it.