pwc-archive/papers-with-abstracts huggingface dataset:
Student Dropout Risk Assessment in Undergraduate Course at Residential University
Authors: Sweta Rai
Date: 2014-05-15 00:00:00
Tasks: Descriptive, Student dropout

Student dropout prediction is an indispensable for numerous intelligent
systems to measure the education system and success rate of any university as
well as throughout the university in the world. Therefore, it becomes essential
to develop efficient methods for prediction of the students at risk of dropping
out, enabling the adoption of proactive process to minimize the situation.
Thus, this research work propose a prototype machine learning tool which can
automatically recognize whether the student will continue their study or drop
their study using classification technique based on decision tree and extract
hidden information from large data about what factors are responsible for
dropout student. Further the contribution of factors responsible for dropout
risk was studied using discriminant analysis and to extract interesting
correlations, frequent patterns, associations or casual structures among
significant datasets, Association rule mining was applied. In this study, the
descriptive statistics analysis was carried out to measure the quality of data
using SPSS 20.0 statistical software and application of decision tree and
association rule were carried out by using WEKA data mining tool.
Topic words analysis based on LDA model
Authors: Xi Qiu, Christopher Stewart
Date: 2014-05-15 00:00:00
Tasks: model

Social network analysis (SNA), which is a research field describing and
modeling the social connection of a certain group of people, is popular among
network services. Our topic words analysis project is a SNA method to visualize
the topic words among emails from Obama.com to accounts registered in Columbus,
Ohio. Based on Latent Dirichlet Allocation (LDA) model, a popular topic model
of SNA, our project characterizes the preference of senders for target group of
receptors. Gibbs sampling is used to estimate topic and word distribution. Our
training and testing data are emails from the carbon-free server
Datagreening.com. We use parallel computing tool BashReduce for word processing
and generate related words under each latent topic to discovers typical
information of political news sending specially to local Columbus receptors.
Running on two instances using paralleling tool BashReduce, our project
contributes almost 30% speedup processing the raw contents, comparing with
processing contents on one instance locally. Also, the experimental result
shows that the LDA model applied in our project provides precision rate 53.96%
higher than TF-IDF model finding target words, on the condition that
appropriate size of topic words list is selected.
Computing Consensus Curves
Authors: Livio De La Cruz, Stephen Kobourov, Sergey Pupyrev, Paul Shen, Sankar Veeramoni
Date: 2012-12-05 00:00:00

We consider the problem of extracting accurate average ant trajectories from
many (possibly inaccurate) input trajectories contributed by citizen
scientists. Although there are many generic software tools for motion tracking
and specific ones for insect tracking, even untrained humans are much better at
this task, provided a robust method to computing the average trajectories. We
implemented and tested several local (one ant at a time) and global (all ants
together) method. Our best performing algorithm uses a novel global method,
based on finding edge-disjoint paths in an ant-interaction graph constructed
from the input trajectories. The underlying optimization problem is a new and
interesting variant of network flow. Even though the problem is NP-hard, we
implemented two heuristics, which work very well in practice, outperforming all
other approaches, including the best automated system.
Logic and Constraint Logic Programming for Distributed Constraint Optimization
Authors: Tiep Le, Enrico Pontelli, Tran Cao Son, William Yeoh
Date: 2014-05-07 00:00:00

The field of Distributed Constraint Optimization Problems (DCOPs) has gained
momentum, thanks to its suitability in capturing complex problems (e.g.,
multi-agent coordination and resource allocation problems) that are naturally
distributed and cannot be realistically addressed in a centralized manner. The
state of the art in solving DCOPs relies on the use of ad-hoc infrastructures
and ad-hoc constraint solving procedures. This paper investigates an
infrastructure for solving DCOPs that is completely built on logic programming
technologies. In particular, the paper explores the use of a general constraint
solver (a constraint logic programming system in this context) to handle the
agent-level constraint solving. The preliminary experiments show that logic
programming provides benefits over a state-of-the-art DCOP system, in terms of
performance and scalability, opening the doors to the use of more advanced
technology (e.g., search strategies and complex constraints) for solving DCOPs.
Understanding Alternating Minimization for Matrix Completion
Authors: Moritz Hardt
Date: 2013-12-03 00:00:00
Tasks: Matrix Completion

Alternating Minimization is a widely used and empirically successful
heuristic for matrix completion and related low-rank optimization problems.
Theoretical guarantees for Alternating Minimization have been hard to come by
and are still poorly understood. This is in part because the heuristic is
iterative and non-convex in nature. We give a new algorithm based on
Alternating Minimization that provably recovers an unknown low-rank matrix from
a random subsample of its entries under a standard incoherence assumption. Our
results reduce the sample size requirements of the Alternating Minimization
approach by at least a quartic factor in the rank and the condition number of
the unknown matrix. These improvements apply even if the matrix is only close
to low-rank in the Frobenius norm. Our algorithm runs in nearly linear time in
the dimension of the matrix and, in a broad range of parameters, gives the
strongest sample bounds among all subquadratic time algorithms that we are
aware of.
  Underlying our work is a new robust convergence analysis of the well-known
Power Method for computing the dominant singular vectors of a matrix. This
viewpoint leads to a conceptually simple understanding of Alternating
Minimization. In addition, we contribute a new technique for controlling the
coherence of intermediate solutions arising in iterative algorithms based on a
smoothed analysis of the QR factorization. These techniques may be of interest
beyond their application here.
Monte Carlo non local means: Random sampling for large-scale image filtering
Authors: Stanley H. Chan, Todd Zickler, Yue M. Lu
Date: 2013-12-27 00:00:00
Tasks: Denoising, Image Denoising

We propose a randomized version of the non-local means (NLM) algorithm for
large-scale image filtering. The new algorithm, called Monte Carlo non-local
means (MCNLM), speeds up the classical NLM by computing a small subset of image
patch distances, which are randomly selected according to a designed sampling
pattern. We make two contributions. First, we analyze the performance of the
MCNLM algorithm and show that, for large images or large external image
databases, the random outcomes of MCNLM are tightly concentrated around the
deterministic full NLM result. In particular, our error probability bounds show
that, at any given sampling ratio, the probability for MCNLM to have a large
deviation from the original NLM solution decays exponentially as the size of
the image or database grows. Second, we derive explicit formulas for optimal
sampling patterns that minimize the error probability bound by exploiting
partial knowledge of the pairwise similarity weights. Numerical experiments
show that MCNLM is competitive with other state-of-the-art fast NLM algorithms
for single-image denoising. When applied to denoising images using an external
database containing ten billion patches, MCNLM returns a randomized solution
that is within 0.2 dB of the full NLM solution while reducing the runtime by
three orders of magnitude.
Exchanging Conflict Resolution in an Adaptable Implementation of ACT-R
Authors: Daniel Gall, Thom Frühwirth
Date: 2014-05-14 00:00:00

In computational cognitive science, the cognitive architecture ACT-R is very
popular. It describes a model of cognition that is amenable to computer
implementation, paving the way for computational psychology. Its underlying
psychological theory has been investigated in many psychological experiments,
but ACT-R lacks a formal definition of its underlying concepts from a
mathematical-computational point of view. Although the canonical implementation
of ACT-R is now modularized, this production rule system is still hard to adapt
and extend in central components like the conflict resolution mechanism (which
decides which of the applicable rules to apply next).
  In this work, we present a concise implementation of ACT-R based on
Constraint Handling Rules which has been derived from a formalization in prior
work. To show the adaptability of our approach, we implement several different
conflict resolution mechanisms discussed in the ACT-R literature. This results
in the first implementation of one such mechanism. For the other mechanisms, we
empirically evaluate if our implementation matches the results of reference
implementations of ACT-R.
Credal Model Averaging for classification: representing prior ignorance and expert opinions
Authors: Giorgio Corani, Andrea Mignatti
Date: 2014-05-14 00:00:00
Tasks: General Classification, Small Data Image Classification

Bayesian model averaging (BMA) is the state of the art approach for
overcoming model uncertainty. Yet, especially on small data sets, the results
yielded by BMA might be sensitive to the prior over the models. Credal Model
Averaging (CMA) addresses this problem by substituting the single prior over
the models by a set of priors (credal set). Such approach solves the problem of
how to choose the prior over the models and automates sensitivity analysis. We
discuss various CMA algorithms for building an ensemble of logistic regressors
characterized by different sets of covariates. We show how CMA can be
appropriately tuned to the case in which one is prior-ignorant and to the case
in which instead domain knowledge is available. CMA detects prior-dependent
instances, namely instances in which a different class is more probable
depending on the prior over the models. On such instances CMA suspends the
judgment, returning multiple classes. We thoroughly compare different BMA and
CMA variants on a real case study, predicting presence of Alpine marmot burrows
in an Alpine valley. We find that BMA is almost a random guesser on the
instances recognized as prior-dependent by CMA.
Improving offline evaluation of contextual bandit algorithms via bootstrapping techniques
Authors: Olivier Nicol, Jérémie Mary, Philippe Preux
Date: 2014-05-14 00:00:00
Tasks: News Recommendation, Recommendation Systems

In many recommendation applications such as news recommendation, the items
that can be rec- ommended come and go at a very fast pace. This is a challenge
for recommender systems (RS) to face this setting. Online learning algorithms
seem to be the most straight forward solution. The contextual bandit framework
was introduced for that very purpose. In general the evaluation of a RS is a
critical issue. Live evaluation is of- ten avoided due to the potential loss of
revenue, hence the need for offline evaluation methods. Two options are
available. Model based meth- ods are biased by nature and are thus difficult to
trust when used alone. Data driven methods are therefore what we consider here.
Evaluat- ing online learning algorithms with past data is not simple but some
methods exist in the litera- ture. Nonetheless their accuracy is not satisfac-
tory mainly due to their mechanism of data re- jection that only allow the
exploitation of a small fraction of the data. We precisely address this issue
in this paper. After highlighting the limita- tions of the previous methods, we
present a new method, based on bootstrapping techniques. This new method comes
with two important improve- ments: it is much more accurate and it provides a
measure of quality of its estimation. The latter is a highly desirable property
in order to minimize the risks entailed by putting online a RS for the first
time. We provide both theoretical and ex- perimental proofs of its superiority
compared to state-of-the-art methods, as well as an analysis of the convergence
of the measure of quality.
Temporal Analysis of Language through Neural Language Models
Authors: Yoon Kim, Yi-I Chiu, Kentaro Hanaki, Darshan Hegde, Slav Petrov
Date: 2014-05-14 00:00:00
Proceeding: WS 2014 6
Tasks: Language Modeling, Language Modelling

We provide a method for automatically detecting change in language across
time through a chronologically trained neural language model. We train the
model on the Google Books Ngram corpus to obtain word vector representations
specific to each year, and identify words that have changed significantly from
1900 to 2009. The model identifies words such as "cell" and "gay" as having
changed during that time period. The model simultaneously identifies the
specific years during which such words underwent change.
COCOpf: An Algorithm Portfolio Framework
Authors: Petr Baudiš
Date: 2014-05-14 00:00:00

Algorithm portfolios represent a strategy of composing multiple heuristic
algorithms, each suited to a different class of problems, within a single
general solver that will choose the best suited algorithm for each input. This
approach recently gained popularity especially for solving combinatoric
problems, but optimization applications are still emerging. The COCO platform
of the BBOB workshop series is the current standard way to measure performance
of continuous black-box optimization algorithms.
  As an extension to the COCO platform, we present the Python-based COCOpf
framework that allows composing portfolios of optimization algorithms and
running experiments with different selection strategies. In our framework, we
focus on black-box algorithm portfolio and online adaptive selection. As a
demonstration, we measure the performance of stock SciPy optimization
algorithms and the popular CMA algorithm alone and in a portfolio with two
simple selection strategies. We confirm that even a naive selection strategy
can provide improved performance across problem classes.
ESmodels: An Epistemic Specification Solver
Authors: Zhizheng Zhang, Kaikai Zhao
Date: 2014-05-14 00:00:00

(To appear in Theory and Practice of Logic Programming (TPLP))
  ESmodels is designed and implemented as an experiment platform to investigate
the semantics, language, related reasoning algorithms, and possible
applications of epistemic specifications.We first give the epistemic
specification language of ESmodels and its semantics. The language employs only
one modal operator K but we prove that it is able to represent luxuriant modal
operators by presenting transformation rules. Then, we describe basic
algorithms and optimization approaches used in ESmodels. After that, we discuss
possible applications of ESmodels in conformant planning and constraint
satisfaction. Finally, we conclude with perspectives.
Measuring Global Similarity between Texts
Authors: Uli Fahrenberg, Fabrizio Biondi, Kevin Corre, Cyrille Jegourel, Simon Kongshøj, Axel Legay
Date: 2014-03-17 00:00:00

We propose a new similarity measure between texts which, contrary to the
current state-of-the-art approaches, takes a global view of the texts to be
compared. We have implemented a tool to compute our textual distance and
conducted experiments on several corpuses of texts. The experiments show that
our methods can reliably identify different global types of texts.
Developing Corpus-based Translation Methods between Informal and Formal Mathematics: Project Description
Authors: Cezary Kaliszyk, Josef Urban, Jiri Vyskocil, Herman Geuvers
Date: 2014-05-14 00:00:00
Tasks: Machine Translation, Translation

The goal of this project is to (i) accumulate annotated informal/formal
mathematical corpora suitable for training semi-automated translation between
informal and formal mathematics by statistical machine-translation methods,
(ii) to develop such methods oriented at the formalization task, and in
particular (iii) to combine such methods with learning-assisted automated
reasoning that will serve as a strong semantic component. We describe these
ideas, the initial set of corpora, and some initial experiments done over them.
Efficient classification using parallel and scalable compressed model and Its application on intrusion detection
Authors: Tieming Chen, Xu Zhang, Shichao Jin, Okhee Kim
Date: 2014-05-14 00:00:00
Tasks: Attribute, Classification, General Classification, Intrusion Detection, Model Compression

In order to achieve high efficiency of classification in intrusion detection,
a compressed model is proposed in this paper which combines horizontal
compression with vertical compression. OneR is utilized as horizontal
com-pression for attribute reduction, and affinity propagation is employed as
vertical compression to select small representative exemplars from large
training data. As to be able to computationally compress the larger volume of
training data with scalability, MapReduce based parallelization approach is
then implemented and evaluated for each step of the model compression process
abovementioned, on which common but efficient classification methods can be
directly used. Experimental application study on two publicly available
datasets of intrusion detection, KDD99 and CMDC2012, demonstrates that the
classification using the compressed model proposed can effectively speed up the
detection procedure at up to 184 times, most importantly at the cost of a
minimal accuracy difference with less than 1% on average.
Reducing Dueling Bandits to Cardinal Bandits
Authors: Nir Ailon, Thorsten Joachims, Zohar Karnin
Date: 2014-05-14 00:00:00
Tasks: Multi-Armed Bandits

We present algorithms for reducing the Dueling Bandits problem to the
conventional (stochastic) Multi-Armed Bandits problem. The Dueling Bandits
problem is an online model of learning with ordinal feedback of the form "A is
preferred to B" (as opposed to cardinal feedback like "A has value 2.5"),
giving it wide applicability in learning from implicit user feedback and
revealed and stated preferences. In contrast to existing algorithms for the
Dueling Bandits problem, our reductions -- named $\Doubler$, $\MultiSbm$ and
$\DoubleSbm$ -- provide a generic schema for translating the extensive body of
known results about conventional Multi-Armed Bandit algorithms to the Dueling
Bandits setting. For $\Doubler$ and $\MultiSbm$ we prove regret upper bounds in
both finite and infinite settings, and conjecture about the performance of
$\DoubleSbm$ which empirically outperforms the other two as well as previous
algorithms in our experiments. In addition, we provide the first almost optimal
regret bound in terms of second order terms, such as the differences between
the values of the arms.
Active Mining of Parallel Video Streams
Authors: Samaneh Khoshrou, Jaime S. Cardoso, Luis F. Teixeira
Date: 2014-05-14 00:00:00
Tasks: Incremental Learning

The practicality of a video surveillance system is adversely limited by the
amount of queries that can be placed on human resources and their vigilance in
response. To transcend this limitation, a major effort under way is to include
software that (fully or at least semi) automatically mines video footage,
reducing the burden imposed to the system. Herein, we propose a semi-supervised
incremental learning framework for evolving visual streams in order to develop
a robust and flexible track classification system. Our proposed method learns
from consecutive batches by updating an ensemble in each time. It tries to
strike a balance between performance of the system and amount of data which
needs to be labelled. As no restriction is considered, the system can address
many practical problems in an evolving multi-camera scenario, such as concept
drift, class evolution and various length of video streams which have not been
addressed before. Experiments were performed on synthetic as well as real-world
visual data in non-stationary environments, showing high accuracy with fairly
little human collaboration.
Learning rates for the risk of kernel based quantile regression estimators in additive models
Authors: Andreas Christmann, Ding-Xuan Zhou
Date: 2014-05-14 00:00:00
Tasks: Additive models, quantile regression, regression, valid

Additive models play an important role in semiparametric statistics. This
paper gives learning rates for regularized kernel based methods for additive
models. These learning rates compare favourably in particular in high
dimensions to recent results on optimal learning rates for purely nonparametric
regularized kernel based quantile regression using the Gaussian radial basis
function kernel, provided the assumption of an additive model is valid.
Additionally, a concrete example is presented to show that a Gaussian function
depending only on one variable lies in a reproducing kernel Hilbert space
generated by an additive Gaussian kernel, but does not belong to the
reproducing kernel Hilbert space generated by the multivariate Gaussian kernel
of the same variance.
Probabilistic Argumentation with Epistemic Extensions and Incomplete Information
Authors: Anthony Hunter, Matthias Thimm
Date: 2014-05-14 00:00:00
Tasks: Abstract Argumentation, valid

Abstract argumentation offers an appealing way of representing and evaluating
arguments and counterarguments. This approach can be enhanced by a probability
assignment to each argument. There are various interpretations that can be
ascribed to this assignment. In this paper, we regard the assignment as
denoting the belief that an agent has that an argument is justifiable, i.e.,
that both the premises of the argument and the derivation of the claim of the
argument from its premises are valid. This leads to the notion of an epistemic
extension which is the subset of the arguments in the graph that are believed
to some degree (which we defined as the arguments that have a probability
assignment greater than 0.5). We consider various constraints on the
probability assignment. Some constraints correspond to standard notions of
extensions, such as grounded or stable extensions, and some constraints give us
new kinds of extensions.
G-AMA: Sparse Gaussian graphical model estimation via alternating minimization
Authors: Onkar Dalal, Bala Rajaratnam
Date: 2014-05-13 00:00:00

Several methods have been recently proposed for estimating sparse Gaussian
graphical models using $\ell_{1}$ regularization on the inverse covariance
matrix. Despite recent advances, contemporary applications require methods that
are even faster in order to handle ill-conditioned high dimensional modern day
datasets. In this paper, we propose a new method, G-AMA, to solve the sparse
inverse covariance estimation problem using Alternating Minimization Algorithm
(AMA), that effectively works as a proximal gradient algorithm on the dual
problem. Our approach has several novel advantages over existing methods.
First, we demonstrate that G-AMA is faster than the previous best algorithms by
many orders of magnitude and is thus an ideal approach for modern high
throughput applications. Second, global linear convergence of G-AMA is
demonstrated rigorously, underscoring its good theoretical properties. Third,
the dual algorithm operates on the covariance matrix, and thus easily
facilitates incorporating additional constraints on pairwise/marginal
relationships between feature pairs based on domain specific knowledge. Over
and above estimating a sparse inverse covariance matrix, we also illustrate how
to (1) incorporate constraints on the (bivariate) correlations and, (2)
incorporate equality (equisparsity) or linear constraints between individual
inverse covariance elements. Fourth, we also show that G-AMA is better adept at
handling extremely ill-conditioned problems, as is often the case with real
data. The methodology is demonstrated on both simulated and real datasets to
illustrate its superior performance over recently proposed methods.