pwc-archive/papers-with-abstracts huggingface dataset:
Particles Prefer Walking Along the Axes: Experimental Insights into the Behavior of a Particle Swarm
Authors: Manuel Schmitt, Rolf Wanka
Date: 2013-03-25 00:00:00

Particle swarm optimization (PSO) is a widely used nature-inspired
meta-heuristic for solving continuous optimization problems. However, when
running the PSO algorithm, one encounters the phenomenon of so-called
stagnation, that means in our context, the whole swarm starts to converge to a
solution that is not (even a local) optimum. The goal of this work is to point
out possible reasons why the swarm stagnates at these non-optimal points. To
achieve our results, we use the newly defined potential of a swarm. The total
potential has a portion for every dimension of the search space, and it drops
when the swarm approaches the point of convergence. As it turns out
experimentally, the swarm is very likely to come sometimes into "unbalanced"
states, i. e., almost all potential belongs to one axis. Therefore, the swarm
becomes blind for improvements still possible in any other direction. Finally,
we show how in the light of the potential and these observations, a slightly
adapted PSO rebalances the potential and therefore increases the quality of the
solution.
Satellite image classification methods and Landsat 5TM bands
Authors: Jamshid Tamouk, Nasser Lotfi, Mina Farmanbar
Date: 2013-08-08 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Satellite Image Classification

This paper attempts to find the most accurate classification method among
parallelepiped, minimum distance and chain methods. Moreover, this study also
challenges to find the suitable combination of bands, which can lead to better
results in case combinations of bands occur. After comparing these three
methods, the chain method over perform the other methods with 79% overall
accuracy. Hence, it is more accurate than minimum distance with 67% and
parallelepiped with 65%. On the other hand, based on bands features, and also
by combining several researchers' findings, a table was created which includes
the main objects on the land and the suitable combination of the bands for
accurately detecting of landcover objects. During this process, it was observed
that band 4 (out of 7 bands of Landsat 5TM) is the band, which can be used for
increasing the accuracy of the combined bands in detecting objects on the land.
OFF-Set: One-pass Factorization of Feature Sets for Online Recommendation in Persistent Cold Start Settings
Authors: Michal Aharon, Natalie Aizenberg, Edward Bortnikov, Ronny Lempel, Roi Adadi, Tomer Benyamini, Liron Levin, Ran Roth, Ohad Serfaty
Date: 2013-08-08 00:00:00

One of the most challenging recommendation tasks is recommending to a new,
previously unseen user. This is known as the 'user cold start' problem.
Assuming certain features or attributes of users are known, one approach for
handling new users is to initially model them based on their features.
  Motivated by an ad targeting application, this paper describes an extreme
online recommendation setting where the cold start problem is perpetual. Every
user is encountered by the system just once, receives a recommendation, and
either consumes or ignores it, registering a binary reward.
  We introduce One-pass Factorization of Feature Sets, OFF-Set, a novel
recommendation algorithm based on Latent Factor analysis, which models users by
mapping their features to a latent space. Furthermore, OFF-Set is able to model
non-linear interactions between pairs of features. OFF-Set is designed for
purely online recommendation, performing lightweight updates of its model per
each recommendation-reward observation. We evaluate OFF-Set against several
state of the art baselines, and demonstrate its superiority on real
ad-targeting data.
Near-Optimal Algorithms for Differentially-Private Principal Components
Authors: Kamalika Chaudhuri, Anand D. Sarwate, Kaushik Sinha
Date: 2012-07-12 00:00:00

Principal components analysis (PCA) is a standard tool for identifying good
low-dimensional approximations to data in high dimension. Many data sets of
interest contain private or sensitive information about individuals. Algorithms
which operate on such data should be sensitive to the privacy risks in
publishing their outputs. Differential privacy is a framework for developing
tradeoffs between privacy and the utility of these outputs. In this paper we
investigate the theory and empirical performance of differentially private
approximations to PCA and propose a new method which explicitly optimizes the
utility of the output. We show that the sample complexity of the proposed
method differs from the existing procedure in the scaling with the data
dimension, and that our method is nearly optimal in terms of this scaling. We
furthermore illustrate our results, showing that on real data there is a large
performance gap between the existing method and our method.
Logical analysis of natural language semantics to solve the problem of computer understanding
Authors: Yuriy Ostapov
Date: 2013-08-07 00:00:00
Tasks: Natural Language Understanding

An object--oriented approach to create a natural language understanding
system is considered. The understanding program is a formal system built on the
base of predicative calculus. Horn's clauses are used as well--formed formulas.
An inference is based on the principle of resolution. Sentences of natural
language are represented in the view of typical predicate set. These predicates
describe physical objects and processes, abstract objects, categories and
semantic relations between objects. Predicates for concrete assertions are
saved in a database. To describe the semantics of classes for physical objects,
abstract concepts and processes, a knowledge base is applied. The proposed
representation of natural language sentences is a semantic net. Nodes of such
net are typical predicates. This approach is perspective as, firstly, such
typification of nodes facilitates essentially forming of processing algorithms
and object descriptions, secondly, the effectiveness of algorithms is increased
(particularly for the great number of nodes), thirdly, to describe the
semantics of words, encyclopedic knowledge is used, and this permits
essentially to extend the class of solved problems.
A Multi-Swarm Cellular PSO based on Clonal Selection Algorithm in Dynamic Environments
Authors: Somayeh Nabizadeh, Alireza Rezvanian, Mohammd Reza Meybodi
Date: 2013-08-07 00:00:00

Many real-world problems are dynamic optimization problems. In this case, the
optima in the environment change dynamically. Therefore, traditional
optimization algorithms disable to track and find optima. In this paper, a new
multi-swarm cellular particle swarm optimization based on clonal selection
algorithm (CPSOC) is proposed for dynamic environments. In the proposed
algorithm, the search space is partitioned into cells by a cellular automaton.
Clustered particles in each cell, which make a sub-swarm, are evolved by the
particle swarm optimization and clonal selection algorithm. Experimental
results on Moving Peaks Benchmark demonstrate the superiority of the CPSOC its
popular methods.
Bayesian ensemble learning for image denoising
Authors: Hyuntaek Oh
Date: 2013-08-06 00:00:00
Tasks: Denoising, Ensemble Learning, Image Denoising

Natural images are often affected by random noise and image denoising has
long been a central topic in Computer Vision. Many algorithms have been
introduced to remove the noise from the natural images, such as Gaussian,
Wiener filtering and wavelet thresholding. However, many of these algorithms
remove the fine edges and make them blur. Recently, many promising denoising
algorithms have been introduced such as Non-local Means, Fields of Experts, and
BM3D. In this paper, we explore Bayesian method of ensemble learning for image
denoising. Ensemble methods seek to combine multiple different algorithms to
retain the strengths of all methods and the weaknesses of none. Bayesian
ensemble models are Non-local Means and Fields of Experts, the very successful
recent algorithms. The Non-local Means presumes that the image contains an
extensive amount of self-similarity. The approach of the Fields of Experts
model extends traditional Markov Random Field model by learning potential
functions over extended pixel neighborhoods. The two models are implemented and
image denoising is performed on natural images. The experimental results
obtained are used to compare with the single algorithm and discuss the ensemble
learning and their approaches. Comparing to the results of Non-local Means and
Fields of Experts, Ensemble learning showed improvement nearly 1dB.
Invariances of random fields paths, with applications in Gaussian Process Regression
Authors: David Ginsbourger, Olivier Roustant, Nicolas Durrande
Date: 2013-08-06 00:00:00
Tasks: regression

We study pathwise invariances of centred random fields that can be controlled
through the covariance. A result involving composition operators is obtained in
second-order settings, and we show that various path properties including
additivity boil down to invariances of the covariance kernel. These results are
extended to a broader class of operators in the Gaussian case, via the Lo\`eve
isometry. Several covariance-driven pathwise invariances are illustrated,
including fields with symmetric paths, centred paths, harmonic paths, or sparse
paths. The proposed approach delivers a number of promising results and
perspectives in Gaussian process regression.
The evolution of representation in simple cognitive networks
Authors: Lars Marstaller, Arend Hintze, Christoph Adami
Date: 2012-06-25 00:00:00

Representations are internal models of the environment that can provide
guidance to a behaving agent, even in the absence of sensory information. It is
not clear how representations are developed and whether or not they are
necessary or even essential for intelligent behavior. We argue here that the
ability to represent relevant features of the environment is the expected
consequence of an adaptive process, give a formal definition of representation
based on information theory, and quantify it with a measure R. To measure how R
changes over time, we evolve two types of networks---an artificial neural
network and a network of hidden Markov gates---to solve a categorization task
using a genetic algorithm. We find that the capacity to represent increases
during evolutionary adaptation, and that agents form representations of their
environment during their lifetime. This ability allows the agents to act on
sensorial inputs in the context of their acquired representations and enables
complex and context-dependent behavior. We examine which concepts (features of
the environment) our networks are representing, how the representations are
logically encoded in the networks, and how they form as an agent behaves to
solve a task. We conclude that R should be able to quantify the representations
within any cognitive system, and should be predictive of an agent's long-term
adaptive success.
Science Fiction as a Worldwide Phenomenon: A Study of International Creation, Consumption and Dissemination
Authors: Elysia Wells
Date: 2013-08-06 00:00:00
Tasks: Survey

This paper examines the international nature of science fiction. The focus of
this research is to determine whether science fiction is primarily English
speaking and Western or global; being created and consumed by people in
non-Western, non-English speaking countries? Science fiction's international
presence was found in three ways, by network analysis, by examining a online
retailer and with a survey. Condor, a program developed by GalaxyAdvisors was
used to determine if science fiction is being talked about by non-English
speakers. An analysis of the international Amazon.com websites was done to
discover if it was being consumed worldwide. A survey was also conducted to see
if people had experience with science fiction. All three research methods
revealed similar results. Science fiction was found to be international, with
science fiction creators originating in different countries and writing in a
host of different languages. English and non-English science fiction was being
created and consumed all over the world, not just in the English speaking West.
Pattern recognition issues on anisotropic smoothed particle hydrodynamics
Authors: Eraldo Pereira Marinho
Date: 2013-08-06 00:00:00

This is a preliminary theoretical discussion on the computational
requirements of the state of the art smoothed particle hydrodynamics (SPH) from
the optics of pattern recognition and artificial intelligence. It is pointed
out in the present paper that, when including anisotropy detection to improve
resolution on shock layer, SPH is a very peculiar case of unsupervised machine
learning. On the other hand, the free particle nature of SPH opens an
opportunity for artificial intelligence to study particles as agents acting in
a collaborative framework in which the timed outcomes of a fluid simulation
forms a large knowledge base, which might be very attractive in computational
astrophysics phenomenological problems like self-propagating star formation.
Spatial-Aware Dictionary Learning for Hyperspectral Image Classification
Authors: Ali Soltani-Farani, Hamid R. Rabiee, Seyyed Abbas Hosseini
Date: 2013-08-06 00:00:00
Tasks: Classification, Dictionary Learning, General Classification, Hyperspectral Image Classification, image-classification, Image Classification

This paper presents a structured dictionary-based model for hyperspectral
data that incorporates both spectral and contextual characteristics of a
spectral sample, with the goal of hyperspectral image classification. The idea
is to partition the pixels of a hyperspectral image into a number of spatial
neighborhoods called contextual groups and to model each pixel with a linear
combination of a few dictionary elements learned from the data. Since pixels
inside a contextual group are often made up of the same materials, their linear
combinations are constrained to use common elements from the dictionary. To
this end, dictionary learning is carried out with a joint sparse regularizer to
induce a common sparsity pattern in the sparse coefficients of each contextual
group. The sparse coefficients are then used for classification using a linear
SVM. Experimental results on a number of real hyperspectral images confirm the
effectiveness of the proposed representation for hyperspectral image
classification. Moreover, experiments with simulated multispectral data show
that the proposed model is capable of finding representations that may
effectively be used for classification of multispectral-resolution samples.
A Linear-Programming Approximation of AC Power Flows
Authors: Carleton Coffrin, Pascal Van Hentenryck
Date: 2012-06-16 00:00:00

Linear active-power-only DC power flow approximations are pervasive in the
planning and control of power systems. However, these approximations fail to
capture reactive power and voltage magnitudes, both of which are necessary in
many applications to ensure voltage stability and AC power flow feasibility.
This paper proposes linear-programming models (the LPAC models) that
incorporate reactive power and voltage magnitudes in a linear power flow
approximation. The LPAC models are built on a convex approximation of the
cosine terms in the AC equations, as well as Taylor approximations of the
remaining nonlinear terms. Experimental comparisons with AC solutions on a
variety of standard IEEE and MatPower benchmarks show that the LPAC models
produce accurate values for active and reactive power, phase angles, and
voltage magnitudes. The potential benefits of the LPAC models are illustrated
on two "proof-of-concept" studies in power restoration and capacitor placement.
Multimodal Approach for Video Surveillance Indexing and Retrieval
Authors: Ali Wali, Adel M. ALIMI
Date: 2013-08-06 00:00:00
Tasks: Retrieval, Video Retrieval

In this paper, we present an overview of a multimodal system to indexing and
searching video sequence by the content that has been developed within the
REGIMVid project. A large part of our system has been developed as part of
TRECVideo evaluation. The MAVSIR platform provides High-level feature
extraction from audio-visual content and concept/event-based video retrieval.
We illustrate the architecture of the system as well as provide an overview of
the descriptors supported to date. Then we demonstrate the usefulness of the
toolbox in the context of feature extraction, concepts/events learning and
retrieval in large collections of video surveillance dataset. The results are
encouraging as we are able to get good results on several event categories,
while for all events we have gained valuable insights and experience.
Image interpolation using Shearlet based iterative refinement
Authors: H. Lakshman, W. -Q Lim, H. Schwarz, D. Marpe, G. Kutyniok, T. Wiegand
Date: 2013-08-05 00:00:00

This paper proposes an image interpolation algorithm exploiting sparse
representation for natural images. It involves three main steps: (a) obtaining
an initial estimate of the high resolution image using linear methods like FIR
filtering, (b) promoting sparsity in a selected dictionary through iterative
thresholding, and (c) extracting high frequency information from the
approximation to refine the initial estimate. For the sparse modeling, a
shearlet dictionary is chosen to yield a multiscale directional representation.
The proposed algorithm is compared to several state-of-the-art methods to
assess its objective as well as subjective performance. Compared to the cubic
spline interpolation method, an average PSNR gain of around 0.8 dB is observed
over a dataset of 200 images.
Theoretical Issues for Global Cumulative Treatment Analysis (GCTA)
Authors: Jeff Shrager
Date: 2013-08-05 00:00:00
Tasks: Decision Making

Adaptive trials are now mainstream science. Recently, researchers have taken
the adaptive trial concept to its natural conclusion, proposing what we call
"Global Cumulative Treatment Analysis" (GCTA). Similar to the adaptive trial,
decision making and data collection and analysis in the GCTA are continuous and
integrated, and treatments are ranked in accord with the statistics of this
information, combined with what offers the most information gain. Where GCTA
differs from an adaptive trial, or, for that matter, from any trial design, is
that all patients are implicitly participants in the GCTA process, regardless
of whether they are formally enrolled in a trial. This paper discusses some of
the theoretical and practical issues that arise in the design of a GCTA, along
with some preliminary thoughts on how they might be approached.
Coevolutionary networks of reinforcement-learning agents
Authors: Ardeshir Kianercy, Aram Galstyan
Date: 2013-08-05 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper presents a model of network formation in repeated games where the
players adapt their strategies and network ties simultaneously using a simple
reinforcement-learning scheme. It is demonstrated that the coevolutionary
dynamics of such systems can be described via coupled replicator equations. We
provide a comprehensive analysis for three-player two-action games, which is
the minimum system size with nontrivial structural dynamics. In particular, we
characterize the Nash equilibria (NE) in such games and examine the local
stability of the rest points corresponding to those equilibria. We also study
general n-player networks via both simulations and analytical methods and find
that in the absence of exploration, the stable equilibria consist of star
motifs as the main building blocks of the network. Furthermore, in all stable
equilibria the agents play pure strategies, even when the game allows mixed NE.
Finally, we study the impact of exploration on learning outcomes, and observe
that there is a critical exploration rate above which the symmetric and
uniformly connected network topology becomes stable.
Sign Stable Projections, Sign Cauchy Projections and Chi-Square Kernels
Authors: Ping Li, Gennady Samorodnitsky, John Hopcroft
Date: 2013-08-05 00:00:00

The method of stable random projections is popular for efficiently computing
the Lp distances in high dimension (where 0<p<=2), using small space. Because
it adopts nonadaptive linear projections, this method is naturally suitable
when the data are collected in a dynamic streaming fashion (i.e., turnstile
data streams). In this paper, we propose to use only the signs of the projected
data and analyze the probability of collision (i.e., when the two signs
differ). We derive a bound of the collision probability which is exact when p=2
and becomes less sharp when p moves away from 2. Interestingly, when p=1 (i.e.,
Cauchy random projections), we show that the probability of collision can be
accurately approximated as functions of the chi-square similarity. For example,
when the (un-normalized) data are binary, the maximum approximation error of
the collision probability is smaller than 0.0192. In text and vision
applications, the chi-square similarity is a popular measure for nonnegative
data when the features are generated from histograms. Our experiments confirm
that the proposed method is promising for large-scale learning applications.
Fast Semidifferential-based Submodular Function Optimization
Authors: Rishabh Iyer, Stefanie Jegelka, Jeff Bilmes
Date: 2013-08-05 00:00:00

We present a practical and powerful new framework for both unconstrained and
constrained submodular function optimization based on discrete
semidifferentials (sub- and super-differentials). The resulting algorithms,
which repeatedly compute and then efficiently optimize submodular
semigradients, offer new and generalize many old methods for submodular
optimization. Our approach, moreover, takes steps towards providing a unifying
paradigm applicable to both submodular min- imization and maximization,
problems that historically have been treated quite distinctly. The practicality
of our algorithms is important since interest in submodularity, owing to its
natural and wide applicability, has recently been in ascendance within machine
learning. We analyze theoretical properties of our algorithms for minimization
and maximization, and show that many state-of-the-art maximization algorithms
are special cases. Lastly, we complement our theoretical analyses with
supporting empirical experiments.
Context Specific Event Model For News Articles
Authors: Kowcika A, Uma Maheswari, Geetha T V
Date: 2013-08-05 00:00:00
Tasks: Articles, Clustering, model

We present a new context based event indexing and event ranking model for
News Articles. The context event clusters formed from the UNL Graphs uses the
modified scoring scheme for segmenting events which is followed by clustering
of events. From the context clusters obtained three models are developed-
Identification of Main and Sub events; Event Indexing and Event Ranking. Based
on the properties considered from the UNL Graphs for the modified scoring main
events and sub events associated with main-events are identified. The temporal
details obtained from the context cluster are stored using hashmap data
structure. The temporal details are place-where the event took; person-who
involved in that event; time-when the event took place. Based on the
information collected from the context clusters three indices are generated-
Time index, Person index, and Place index. This index gives complete details
about every event obtained from context clusters. A new scoring scheme is
introduced for ranking the events. The scoring scheme for event ranking gives
weight-age based on the priority level of the events. The priority level
includes the occurrence of the event in the title of the document, event
frequency, and inverse document frequency of the events.