pwc-archive/papers-with-abstracts huggingface dataset:
Pseudo Dynamic Transitional Modeling of Building Heating Energy Demand Using Artificial Neural Network
Authors: S. Paudel, M. Elmtiri, W. L. Kling, O. Le Corre, B. Lacarriere
Date: 2014-11-17 00:00:00
Tasks: Prediction, Robust Design

This paper presents the building heating demand prediction model with
occupancy profile and operational heating power level characteristics in short
time horizon (a couple of days) using artificial neural network. In addition,
novel pseudo dynamic transitional model is introduced, which consider time
dependent attributes of operational power level characteristics and its effect
in the overall model performance is outlined. Pseudo dynamic model is applied
to a case study of French Institution building and compared its results with
static and other pseudo dynamic neural network models. The results show the
coefficients of correlation in static and pseudo dynamic neural network model
of 0.82 and 0.89 (with energy consumption error of 0.02%) during the learning
phase, and 0.61 and 0.85 during the prediction phase respectively. Further,
orthogonal array design is applied to the pseudo dynamic model to check the
schedule of occupancy profile and operational heating power level
characteristics. The results show the new schedule and provide the robust
design for pseudo dynamic model. Due to prediction in short time horizon, it
finds application for Energy Services Company (ESCOs) to manage the heating
load for dynamic control of heat production system.
AlexU-Word: A New Dataset for Isolated-Word Closed-Vocabulary Offline Arabic Handwriting Recognition
Authors: Mohamed E. Hussein, Marwan Torki, Ahmed Elsallamy, Mahmoud Fayyaz
Date: 2014-11-17 00:00:00
Tasks: All, Handwriting Recognition

In this paper, we introduce the first phase of a new dataset for offline
Arabic handwriting recognition. The aim is to collect a very large dataset of
isolated Arabic words that covers all letters of the alphabet in all possible
shapes using a small number of simple words. The end goal is to collect a very
large dataset of segmented letter images, which can be used to build and
evaluate Arabic handwriting recognition systems that are based on segmented
letter recognition. The current version of the dataset contains $25114$ samples
of $109$ unique Arabic words that cover all possible shapes of all alphabet
letters. The samples were collected from $907$ writers. In its current form,
the dataset can be used for the problem of closed-vocabulary word recognition.
We evaluated a number of window-based descriptors and classifiers on this task
and obtained an accuracy of $92.16\%$ using a SIFT-based descriptor and ANN.
A unifying framework for relaxations of the causal assumptions in Bell's theorem
Authors: Rafael Chaves, Richard Kueng, Jonatan Bohr Brask, David Gross
Date: 2014-11-17 00:00:00

Bell's Theorem shows that quantum mechanical correlations can violate the
constraints that the causal structure of certain experiments impose on any
classical explanation. It is thus natural to ask to which degree the causal
assumptions -- e.g. locality or measurement independence -- have to be relaxed
in order to allow for a classical description of such experiments. Here, we
develop a conceptual and computational framework for treating this problem. We
employ the language of Bayesian networks to systematically construct
alternative causal structures and bound the degree of relaxation using
quantitative measures that originate from the mathematical theory of causality.
The main technical insight is that the resulting problems can often be
expressed as computationally tractable linear programs. We demonstrate the
versatility of the framework by applying it to a variety of scenarios, ranging
from relaxations of the measurement independence, locality and bilocality
assumptions, to a novel causal interpretation of CHSH inequality violations.
State-of-the-Art in Retinal Optical Coherence Tomography Image Analysis
Authors: Ahmadreza Baghaie, Roshan M. D'souza, Zeyun Yu
Date: 2014-11-04 00:00:00
Tasks: Image Registration

Optical Coherence Tomography (OCT) is one of the most emerging imaging
modalities that has been used widely in the field of biomedical imaging. From
its emergence in 1990's, plenty of hardware and software improvements have been
made. Its applications range from ophthalmology to dermatology to coronary
imaging etc. Here, the focus is on applications of OCT in ophthalmology and
retinal imaging. OCT is able to non-invasively produce cross-sectional volume
images of the tissues which are further used for analysis of the tissue
structure and its properties. Due to the underlying physics, OCT images usually
suffer from a granular pattern, called speckle noise, which restricts the
process of interpretation, hence requiring specialized noise reduction
techniques to remove the noise while preserving image details. Also, given the
fact that OCT images are in the $\mu m$ -level, further analysis in needed to
distinguish between the different structures in the imaged volume. Therefore
the use of different segmentation techniques are of high importance. The
movement of the tissue under imaging or the progression of disease in the
tissue also imposes further implications both on the quality and the proper
interpretation of the acquired images. Thus, use of image registration
techniques can be very helpful. In this work, an overview of such image
analysis techniques will be given.
Aggregating partial rankings with applications to peer grading in massive online open courses
Authors: Ioannis Caragiannis, George A. Krimpas, Alexandros A. Voudouris
Date: 2014-11-17 00:00:00

We investigate the potential of using ordinal peer grading for the evaluation
of students in massive online open courses (MOOCs). According to such grading
schemes, each student receives a few assignments (by other students) which she
has to rank. Then, a global ranking (possibly translated into numerical scores)
is produced by combining the individual ones. This is a novel application area
for social choice concepts and methods where the important problem to be solved
is as follows: how should the assignments be distributed so that the collected
individual rankings can be easily merged into a global one that is as close as
possible to the ranking that represents the relative performance of the
students in the assignment? Our main theoretical result suggests that using
very simple ways to distribute the assignments so that each student has to rank
only $k$ of them, a Borda-like aggregation method can recover a $1-O(1/k)$
fraction of the true ranking when each student correctly ranks the assignments
she receives. Experimental results strengthen our analysis further and also
demonstrate that the same method is extremely robust even when students have
imperfect capabilities as graders. We believe that our results provide strong
evidence that ordinal peer grading can be a highly effective and scalable
solution for evaluation in MOOCs.
Relations World: A Possibilistic Graphical Model
Authors: Christopher J. C. Burges, Erin Renshaw, Andrzej Pastusiak
Date: 2014-11-17 00:00:00
Tasks: coreference-resolution, Coreference Resolution, model

We explore the idea of using a "possibilistic graphical model" as the basis
for a world model that drives a dialog system. As a first step we have
developed a system that uses text-based dialog to derive a model of the user's
family relations. The system leverages its world model to infer relational
triples, to learn to recover from upstream coreference resolution errors and
ambiguities, and to learn context-dependent paraphrase models. We also explore
some theoretical aspects of the underlying graphical model.
A Note on Systematic Conflict Generation in CA-EN-type Causal Structures
Authors: Antoni Ligęza
Date: 2014-11-17 00:00:00
Tasks: Diagnostic

This paper is aimed at providing a very first, more "global", systematic
point of view with respect to possible conflict generation in CA-EN-like causal
structures. For simplicity, only the outermost level of graphs is taken into
account. Localization of the "conflict area", diagnostic preferences, and bases
for systematic conflict generation are considered. A notion of {\em Potential
Conflict Structure} ({\em PCS}) constituting a basic tool for identification of
possible conflicts is proposed and its use is discussed.
Joint Association Graph Screening and Decomposition for Large-scale Linear Dynamical Systems
Authors: Yiyuan She, Yuejia He, Shijie Li, Dapeng Wu
Date: 2014-11-17 00:00:00

This paper studies large-scale dynamical networks where the current state of
the system is a linear transformation of the previous state, contaminated by a
multivariate Gaussian noise. Examples include stock markets, human brains and
gene regulatory networks. We introduce a transition matrix to describe the
evolution, which can be translated to a directed Granger transition graph, and
use the concentration matrix of the Gaussian noise to capture the second-order
relations between nodes, which can be translated to an undirected conditional
dependence graph. We propose regularizing the two graphs jointly in topology
identification and dynamics estimation. Based on the notion of joint
association graph (JAG), we develop a joint graphical screening and estimation
(JGSE) framework for efficient network learning in big data. In particular, our
method can pre-determine and remove unnecessary edges based on the joint
graphical structure, referred to as JAG screening, and can decompose a large
network into smaller subnetworks in a robust manner, referred to as JAG
decomposition. JAG screening and decomposition can reduce the problem size and
search space for fine estimation at a later stage. Experiments on both
synthetic data and real-world applications show the effectiveness of the
proposed framework in large-scale network topology identification and dynamics
estimation.
Window-Based Descriptors for Arabic Handwritten Alphabet Recognition: A Comparative Study on a Novel Dataset
Authors: Marwan Torki, Mohamed E. Hussein, Ahmed Elsallamy, Mahmoud Fayyaz, Shehab Yaser
Date: 2014-11-13 00:00:00

This paper presents a comparative study for window-based descriptors on the
application of Arabic handwritten alphabet recognition. We show a detailed
experimental evaluation of different descriptors with several classifiers. The
objective of the paper is to evaluate different window-based descriptors on the
problem of Arabic letter recognition. Our experiments clearly show that they
perform very well. Moreover, we introduce a novel spatial pyramid partitioning
scheme that enhances the recognition accuracy for most descriptors. In
addition, we introduce a novel dataset for Arabic handwritten isolated alphabet
letters, which can serve as a benchmark for future research.
A Parallel Genetic Algorithm for Three Dimensional Bin Packing with Heterogeneous Bins
Authors: Drona Pratap Chandu
Date: 2014-11-17 00:00:00

This paper presents a parallel genetic algorithm for three dimensional bin
packing with heterogeneous bins using Hadoop Map-Reduce framework. The most
common three dimensional bin packing problem which packs given set of boxes
into minimum number of equal sized bins is proven to be NP Hard. The variation
of three dimensional bin packing problem that allows heterogeneous bin sizes
and rotation of boxes is computationally more harder than common three
dimensional bin packing problem. The proposed Map-Reduce implementation helps
to run the genetic algorithm for three dimensional bin packing with
heterogeneous bins on multiple machines parallely and computes the solution in
relatively short time.
Stochastic Blockmodeling for Online Advertising
Authors: Li Chen, Matthew Patton
Date: 2014-10-24 00:00:00
Tasks: Clustering, graph partitioning

Online advertising is an important and huge industry. Having knowledge of the
website attributes can contribute greatly to business strategies for
ad-targeting, content display, inventory purchase or revenue prediction.
Classical inferences on users and sites impose challenge, because the data is
voluminous, sparse, high-dimensional and noisy. In this paper, we introduce a
stochastic blockmodeling for the website relations induced by the event of
online user visitation. We propose two clustering algorithms to discover the
instrinsic structures of websites, and compare the performance with a
goodness-of-fit method and a deterministic graph partitioning method. We
demonstrate the effectiveness of our algorithms on both simulation and AOL
website dataset.
Implicitly Constrained Semi-Supervised Linear Discriminant Analysis
Authors: Jesse H. Krijthe, Marco Loog
Date: 2014-11-17 00:00:00

Semi-supervised learning is an important and active topic of research in
pattern recognition. For classification using linear discriminant analysis
specifically, several semi-supervised variants have been proposed. Using any
one of these methods is not guaranteed to outperform the supervised classifier
which does not take the additional unlabeled data into account. In this work we
compare traditional Expectation Maximization type approaches for
semi-supervised linear discriminant analysis with approaches based on intrinsic
constraints and propose a new principled approach for semi-supervised linear
discriminant analysis, using so-called implicit constraints. We explore the
relationships between these methods and consider the question if and in what
sense we can expect improvement in performance over the supervised procedure.
The constraint based approaches are more robust to misspecification of the
model, and may outperform alternatives that make more assumptions on the data,
in terms of the log-likelihood of unseen objects.
Verification of Relational Multiagent Systems with Data Types (Extended Version)
Authors: Diego Calvanese, Giorgio Delzanno, Marco Montali
Date: 2014-11-17 00:00:00

We study the extension of relational multiagent systems (RMASs), where agents
manipulate full-fledged relational databases, with data types and facets
equipped with domain-specific, rigid relations (such as total orders).
Specifically, we focus on design-time verification of RMASs against rich
first-order temporal properties expressed in a variant of first-order
mu-calculus with quantification across states. We build on previous
decidability results under the "state-bounded" assumption, i.e., in each single
state only a bounded number of data objects is stored in the agent databases,
while unboundedly many can be encountered over time. We recast this condition,
showing decidability in presence of dense, linear orders, and facets defined on
top of them. Our approach is based on the construction of a finite-state, sound
and complete abstraction of the original system, in which dense linear orders
are reformulated as non-rigid relations working on the active domain of the
system only. We also show undecidability when including a data type equipped
with the successor relation.
Parallel Gaussian Process Regression for Big Data: Low-Rank Representation Meets Markov Approximation
Authors: Kian Hsiang Low, Jiangbo Yu, Jie Chen, Patrick Jaillet
Date: 2014-11-17 00:00:00
Tasks: regression

The expressive power of a Gaussian process (GP) model comes at a cost of poor
scalability in the data size. To improve its scalability, this paper presents a
low-rank-cum-Markov approximation (LMA) of the GP model that is novel in
leveraging the dual computational advantages stemming from complementing a
low-rank approximate representation of the full-rank GP based on a support set
of inputs with a Markov approximation of the resulting residual process; the
latter approximation is guaranteed to be closest in the Kullback-Leibler
distance criterion subject to some constraint and is considerably more refined
than that of existing sparse GP models utilizing low-rank representations due
to its more relaxed conditional independence assumption (especially with larger
data). As a result, our LMA method can trade off between the size of the
support set and the order of the Markov property to (a) incur lower
computational cost than such sparse GP models while achieving predictive
performance comparable to them and (b) accurately represent features/patterns
of any scale. Interestingly, varying the Markov order produces a spectrum of
LMAs with PIC approximation and full-rank GP at the two extremes. An advantage
of our LMA method is that it is amenable to parallelization on multiple
machines/cores, thereby gaining greater scalability. Empirical evaluation on
three real-world datasets in clusters of up to 32 computing nodes shows that
our centralized and parallel LMA methods are significantly more time-efficient
and scalable than state-of-the-art sparse and full-rank GP regression methods
while achieving comparable predictive performances.
Spectral Unmixing via Data-guided Sparsity
Authors: Feiyun Zhu, Ying Wang, Bin Fan, Gaofeng Meng, Shiming Xiang, Chunhong Pan
Date: 2014-03-13 00:00:00
Tasks: Hyperspectral Unmixing

Hyperspectral unmixing, the process of estimating a common set of spectral
bases and their corresponding composite percentages at each pixel, is an
important task for hyperspectral analysis, visualization and understanding.
From an unsupervised learning perspective, this problem is very
challenging---both the spectral bases and their composite percentages are
unknown, making the solution space too large. To reduce the solution space,
many approaches have been proposed by exploiting various priors. In practice,
these priors would easily lead to some unsuitable solution. This is because
they are achieved by applying an identical strength of constraints to all the
factors, which does not hold in practice. To overcome this limitation, we
propose a novel sparsity based method by learning a data-guided map to describe
the individual mixed level of each pixel. Through this data-guided map, the
$\ell_{p}(0<p<1)$ constraint is applied in an adaptive manner. Such
implementation not only meets the practical situation, but also guides the
spectral bases toward the pixels under highly sparse constraint. What's more,
an elegant optimization scheme as well as its convergence proof have been
provided in this paper. Extensive experiments on several datasets also
demonstrate that the data-guided map is feasible, and high quality unmixing
results could be obtained by our method.
10,000+ Times Accelerated Robust Subset Selection (ARSS)
Authors: Feiyun Zhu, Bin Fan, Xinliang Zhu, Ying Wang, Shiming Xiang, Chunhong Pan
Date: 2014-09-12 00:00:00
Tasks: 10-shot image generation, Action Recognition, Collaborative Filtering, Fine-Grained Image Classification, Image Classification, Image Generation, Machine Translation, Multimodal Sentiment Analysis, Music Modeling, Named Entity Recognition (NER), Nested Named Entity Recognition, Node Classification, Object Detection, Panoptic Segmentation, Part-Of-Speech Tagging, Pose Estimation, Relation Extraction, RGB Salient Object Detection, Semantic Segmentation, Semi-Supervised Image Classification, Skeleton Based Action Recognition, Temporal Action Proposal Generation, Traffic Prediction, Visual Object Tracking

Subset selection from massive data with noised information is increasingly
popular for various applications. This problem is still highly challenging as
current methods are generally slow in speed and sensitive to outliers. To
address the above two issues, we propose an accelerated robust subset selection
(ARSS) method. Specifically in the subset selection area, this is the first
attempt to employ the $\ell_{p}(0<p\leq1)$-norm based measure for the
representation loss, preventing large errors from dominating our objective. As
a result, the robustness against outlier elements is greatly enhanced.
Actually, data size is generally much larger than feature length, i.e. $N\gg
L$. Based on this observation, we propose a speedup solver (via ALM and
equivalent derivations) to highly reduce the computational cost, theoretically
from $O(N^{4})$ to $O(N{}^{2}L)$. Extensive experiments on ten benchmark
datasets verify that our method not only outperforms state of the art methods,
but also runs 10,000+ times faster than the most related method.
Opinion mining of text documents written in Macedonian language
Authors: Andrej Gajduk, Ljupco Kocarev
Date: 2014-11-17 00:00:00
Tasks: Opinion Mining

The ability to extract public opinion from web portals such as review sites,
social networks and blogs will enable companies and individuals to form a view,
an attitude and make decisions without having to do lengthy and costly
researches and surveys. In this paper machine learning techniques are used for
determining the polarity of forum posts on kajgana which are written in
Macedonian language. The posts are classified as being positive, negative or
neutral. We test different feature metrics and classifiers and provide detailed
evaluation of their participation in improving the overall performance on a
manually generated dataset. By achieving 92% accuracy, we show that the
performance of systems for automated opinion mining is comparable to a human
evaluator, thus making it a viable option for text data analysis. Finally, we
present a few statistics derived from the forum posts using the developed
system.
Fully Convolutional Neural Networks for Crowd Segmentation
Authors: Kai Kang, Xiaogang Wang
Date: 2014-11-17 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation, Translation

In this paper, we propose a fast fully convolutional neural network (FCNN)
for crowd segmentation. By replacing the fully connected layers in CNN with 1
by 1 convolution kernels, FCNN takes whole images as inputs and directly
outputs segmentation maps by one pass of forward propagation. It has the
property of translation invariance like patch-by-patch scanning but with much
lower computation cost. Once FCNN is learned, it can process input images of
any sizes without warping them to a standard size. These attractive properties
make it extendable to other general image segmentation problems. Based on FCNN,
a multi-stage deep learning is proposed to integrate appearance and motion cues
for crowd segmentation. Both appearance filters and motion filers are
pretrained stage-by-stage and then jointly optimized. Different combination
methods are investigated. The effectiveness of our approach and component-wise
analysis are evaluated on two crowd segmentation datasets created by us, which
include image frames from 235 and 11 scenes, respectively. They are currently
the largest crowd segmentation datasets and will be released to the public.
Errata: Distant Supervision for Relation Extraction with Matrix Completion
Authors: Miao Fan, Deli Zhao, Qiang Zhou, Zhiyuan Liu, Thomas Fang Zheng, Edward Y. Chang
Date: 2014-11-17 00:00:00
Tasks: Classification, General Classification, Low-Rank Matrix Completion, Matrix Completion, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Relation, Relation Classification, Relation Extraction

The essence of distantly supervised relation extraction is that it is an
incomplete multi-label classification problem with sparse and noisy features.
To tackle the sparsity and noise challenges, we propose solving the
classification problem using matrix completion on factorized matrix of
minimized rank. We formulate relation classification as completing the unknown
labels of testing items (entity pairs) in a sparse matrix that concatenates
training and testing textual features with training labels. Our algorithmic
framework is based on the assumption that the rank of item-by-feature and
item-by-label joint matrix is low. We apply two optimization models to recover
the underlying low-rank matrix leveraging the sparsity of feature-label matrix.
The matrix completion problem is then solved by the fixed point continuation
(FPC) algorithm, which can find the global optimum. Experiments on two widely
used datasets with different dimensions of textual features demonstrate that
our low-rank matrix completion approach significantly outperforms the baseline
and the state-of-the-art methods.