pwc-archive/papers-with-abstracts huggingface dataset:
Reaserchnig the Development of the Electrical Power System Using Systemically Evolutionary Algorithm
Authors: Jerzy Tchorzewski, Emil Chyzy
Date: 2014-01-21 00:00:00
Tasks: Evolutionary Algorithms

The paper contains the concept and the results of research concerning the
evolutionary algorithm, identified based on the systems control theory, which
was called the Systemically of Evolutionary Algorithm (SAE). Special attention
was paid to two elements of evolutionary algorithms, which have not been fully
solved yet, i.e. to the methods used to create the initial population and the
method of creating the robustness (fitness) function. Other elements of the SEA
algorithm, i.a. cross-over, mutation, selection, etc. were also defined from a
systemic point of view. Computational experiments were conducted using a
selected subsystem of the Polish Electrical Power System and three programming
languages: Java, C++ and Matlab. Selected comparative results for the SAE
algorithm in different implementations were also presented.
Real Time Strategy Language
Authors: Roy Hayes, Peter Beling, William Scherer
Date: 2014-01-21 00:00:00

Real Time Strategy (RTS) games provide complex domain to test the latest
artificial intelligence (AI) research. In much of the literature, AI systems
have been limited to playing one game. Although, this specialization has
resulted in stronger AI gaming systems it does not address the key concerns of
AI researcher. AI researchers seek the development of AI agents that can
autonomously interpret learn, and apply new knowledge. To achieve human level
performance, current AI systems rely on game specific knowledge of an expert.
The paper presents the full RTS language in hopes of shifting the current
research focus to the development of general RTS agents. General RTS agents are
AI gaming systems that can play any RTS games, defined in the RTS language.
This prevents game specific knowledge from being hard coded into the system,
thereby facilitating research that addresses the fundamental concerns of
artificial intelligence.
On change point detection using the fused lasso method
Authors: Cristian R. Rojas, Bo Wahlberg
Date: 2014-01-21 00:00:00
Tasks: Change Point Detection, Denoising, Time Series, Time Series Analysis

In this paper we analyze the asymptotic properties of l1 penalized maximum
likelihood estimation of signals with piece-wise constant mean values and/or
variances. The focus is on segmentation of a non-stationary time series with
respect to changes in these model parameters. This change point detection and
estimation problem is also referred to as total variation denoising or l1 -mean
filtering and has many important applications in most fields of science and
engineering. We establish the (approximate) sparse consistency properties,
including rate of convergence, of the so-called fused lasso signal approximator
(FLSA). We show that this only holds if the sign of the corresponding
consecutive changes are all different, and that this estimator is otherwise
incapable of correctly detecting the underlying sparsity pattern. The key idea
is to notice that the optimality conditions for this problem can be analyzed
using techniques related to brownian bridge theory.
HMACA: Towards Proposing a Cellular Automata Based Tool for Protein Coding, Promoter Region Identification and Protein Structure Prediction
Authors: Pokkuluri Kiran Sree, Inampudi Ramesh Babu, SSSN Usha Devi N
Date: 2014-01-21 00:00:00
Tasks: Protein Structure Prediction

Human body consists of lot of cells, each cell consist of DeOxaRibo Nucleic
Acid (DNA). Identifying the genes from the DNA sequences is a very difficult
task. But identifying the coding regions is more complex task compared to the
former. Identifying the protein which occupy little place in genes is a really
challenging issue. For understating the genes coding region analysis plays an
important role. Proteins are molecules with macro structure that are
responsible for a wide range of vital biochemical functions, which includes
acting as oxygen, cell signaling, antibody production, nutrient transport and
building up muscle fibers. Promoter region identification and protein structure
prediction has gained a remarkable attention in recent years. Even though there
are some identification techniques addressing this problem, the approximate
accuracy in identifying the promoter region is closely 68% to 72%. We have
developed a Cellular Automata based tool build with hybrid multiple attractor
cellular automata (HMACA) classifier for protein coding region, promoter region
identification and protein structure prediction which predicts the protein and
promoter regions with an accuracy of 76%. This tool also predicts the structure
of protein with an accuracy of 80%.
Domain Views for Constraint Programming
Authors: Pascal Van Hentenryck, Laurent Michel
Date: 2014-01-21 00:00:00

Views are a standard abstraction in constraint programming: They make it
possible to implement a single version of each constraint, while avoiding to
create new variables and constraints that would slow down propagation.
Traditional constraint-programming systems provide the concept of {\em variable
views} which implement a view of the type $y = f(x)$ by delegating all (domain
and constraint) operations on variable $y$ to variable $x$. This paper proposes
the alternative concept of {\em domain views} which only delegate domain
operations. Domain views preserve the benefits of variable views but simplify
the implementation of value-based propagation. Domain views also support
non-injective views compositionally, expanding the scope of views
significantly. Experimental results demonstrate the practical benefits of
domain views.
A Microkernel Architecture for Constraint Programming
Authors: Laurent Michel, Pascal Van Hentenryck
Date: 2014-01-21 00:00:00

This paper presents a microkernel architecture for constraint programming
organized around a number of small number of core functionalities and minimal
interfaces. The architecture contrasts with the monolithic nature of many
implementations. Experimental results indicate that the software engineering
benefits are not incompatible with runtime efficiency.
Study of Neural Network Algorithm for Straight-Line Drawings of Planar Graphs
Authors: Mohamed A. El-Sayed, S. Abdel-Khalek, Hanan H. Amin
Date: 2014-01-21 00:00:00

Graph drawing addresses the problem of finding a layout of a graph that
satisfies given aesthetic and understandability objectives. The most important
objective in graph drawing is minimization of the number of crossings in the
drawing, as the aesthetics and readability of graph drawings depend on the
number of edge crossings. VLSI layouts with fewer crossings are more easily
realizable and consequently cheaper. A straight-line drawing of a planar graph
G of n vertices is a drawing of G such that each edge is drawn as a
straight-line segment without edge crossings. However, a problem with current
graph layout methods which are capable of producing satisfactory results for a
wide range of graphs is that they often put an extremely high demand on
computational resources. This paper introduces a new layout method, which
nicely draws internally convex of planar graph that consumes only little
computational resources and does not need any heavy duty preprocessing. Here,
we use two methods: The first is self organizing map known from unsupervised
neural networks which is known as (SOM) and the second method is Inverse Self
Organized Map (ISOM).
Compositional Operators in Distributional Semantics
Authors: Dimitri Kartsaklis
Date: 2014-01-21 00:00:00
Tasks: Diversity

This survey presents in some detail the main advances that have been recently
taking place in Computational Linguistics towards the unification of the two
prominent semantic paradigms: the compositional formal semantics view and the
distributional models of meaning based on vector spaces. After an introduction
to these two approaches, I review the most important models that aim to provide
compositionality in distributional semantics. Then I proceed and present in
more detail a particular framework by Coecke, Sadrzadeh and Clark (2010) based
on the abstract mathematical setting of category theory, as a more complete
example capable to demonstrate the diversity of techniques and scientific
disciplines that this kind of research can draw from. This paper concludes with
a discussion about important open issues that need to be addressed by the
researchers in the future.
Increasing Server Availability for Overall System Security: A Preventive Maintenance Approach Based on Failure Prediction
Authors: Ayman M. Bahaa-Eldin, Hoda K. Mohamead, Sally S. Deraz
Date: 2014-01-21 00:00:00
Tasks: Management, Time Series, Time Series Analysis, Time Series Forecasting

Server Availability (SA) is an important measure of overall systems security.
Important security systems rely on the availability of their hosting servers to
deliver critical security services. Many of these servers offer management
interface through web mainly using an Apache server. This paper investigates
the increase of Server Availability by the use of Artificial Neural Networks
(ANN) to predict software aging phenomenon. Several resource usage data is
collected and analyzed on a typical long-running software system (a web
server). A Multi-Layer Perceptron feed forward Artificial Neural Network was
trained on an Apache web server data-set to predict future server resource
exhaustion through uni-variate time series forecasting. The results were
benchmarked against those obtained from non-parametric statistical techniques,
parametric time series models and empirical modeling techniques reported in the
literature.
Genetic Algorithms and its use with back-propagation network
Authors: Ayman M. Bahaa-Eldin, A. M. A. Wahdan, H. M. K. Mahdi
Date: 2014-01-21 00:00:00

Genetic algorithms are considered as one of the most efficient search
techniques. Although they do not offer an optimal solution, their ability to
reach a suitable solution in considerably short time gives them their
respectable role in many AI techniques. This work introduces genetic algorithms
and describes their characteristics. Then a novel method using genetic
algorithm in best training set generation and selection for a back-propagation
network is proposed. This work also offers a new extension to the original
genetic algorithms
Edge detection of binary images using the method of masks
Authors: Ayman M Bahaa-Eldeen, Abdel-Moneim A. Wahdan, Hani M. K. Mahdi
Date: 2014-01-21 00:00:00
Tasks: Edge Detection

In this work the method of masks, creating and using of inverted image masks,
together with binary operation of image data are used in edge detection of
binary images, monochrome images, which yields about 300 times faster than
ordinary methods. The method is divided into three stages: Mask construction,
Fundamental edge detection, and Edge Construction Comparison with an ordinary
method and a fuzzy based method is carried out.
Optimal Intelligent Control for Wind Turbulence Rejection in WECS Using ANNs and Genetic Fuzzy Approach
Authors: Hadi kasiri, hamid reza momeni, Atiyeh Kasiri
Date: 2014-01-21 00:00:00

One of the disadvantages in Connection of wind energy conversion systems
(WECSs) to transmission networks is plentiful turbulence of wind speed.
Therefore effects of this problem must be controlled. Nowadays,
pitch-controlled WECSs are increasingly used for variable speed and pitch wind
turbines. Megawatt class wind turbines generally turn at variable speed in wind
farm. Thus turbine operation must be controlled in order to maximize the
conversion efficiency below rated power and reduce loading on the drive-train.
Due to random and non-linear nature of the wind turbulence and the ability of
Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) Artificial Neural
Networks (ANNs) in the modeling and control of this turbulence, in this study,
widespread changes of wind have been perused using MLP and RBF artificial NNs.
In addition in this study, a new genetic fuzzy system has been successfully
applied to identify disturbance wind in turbine input. Thus output power has
been regulated in optimal and nominal range by pitch angle regulation.
Consequently, our proposed approaches have regulated output aerodynamic power
and torque in the nominal rang.
Consistency of weighted majority votes
Authors: Daniel Berend, Aryeh Kontorovich
Date: 2013-12-02 00:00:00
Proceeding: NeurIPS 2014 12

We revisit the classical decision-theoretic problem of weighted expert voting
from a statistical learning perspective. In particular, we examine the
consistency (both asymptotic and finitary) of the optimal Nitzan-Paroush
weighted majority and related rules. In the case of known expert competence
levels, we give sharp error estimates for the optimal rule. When the competence
levels are unknown, they must be empirically estimated. We provide frequentist
and Bayesian analyses for this situation. Some of our proof techniques are
non-standard and may be of independent interest. The bounds we derive are
nearly optimal, and several challenging open problems are posed. Experimental
results are provided to illustrate the theory.
Skill Analysis with Time Series Image Data
Authors: Toshiyuki Maeda, Masanori Fujii, Isao Hayashi
Date: 2014-01-21 00:00:00
Tasks: Time Series, Time Series Analysis

We present a skill analysis with time series image data using data mining
methods, focused on table tennis. We do not use body model, but use only
hi-speed movies, from which time series data are obtained and analyzed using
data mining methods such as C4.5 and so on. We identify internal models for
technical skills as evaluation skillfulness for the forehand stroke of table
tennis, and discuss mono and meta-functional skills for improving skills.
Harmony Search Algorithm for Curriculum-Based Course Timetabling Problem
Authors: Juliana Wahid, Naimah Mohd Hussin
Date: 2014-01-21 00:00:00

In this paper, harmony search algorithm is applied to curriculum-based course
timetabling. The implementation, specifically the process of improvisation
consists of memory consideration, random consideration and pitch adjustment. In
memory consideration, the value of the course number for new solution was
selected from all other course number located in the same column of the Harmony
Memory. This research used the highest occurrence of the course number to be
scheduled in a new harmony. The remaining courses that have not been scheduled
by memory consideration will go through random consideration, i.e. will select
any feasible location available to be scheduled in the new harmony solution.
Each course scheduled out of memory consideration is examined as to whether it
should be pitch adjusted with probability of eight procedures. However, the
algorithm produced results that were not comparatively better than those
previously known as best solution. With proper modification in terms of the
approach in this algorithm would make the algorithm perform better on
curriculum-based course timetabling.
A Unifying Framework for Typical Multi-Task Multiple Kernel Learning Problems
Authors: Cong Li, Michael Georgiopoulos, Georgios C. Anagnostopoulos
Date: 2014-01-21 00:00:00
Tasks: feature selection, Multi-Task Learning

Over the past few years, Multi-Kernel Learning (MKL) has received significant
attention among data-driven feature selection techniques in the context of
kernel-based learning. MKL formulations have been devised and solved for a
broad spectrum of machine learning problems, including Multi-Task Learning
(MTL). Solving different MKL formulations usually involves designing algorithms
that are tailored to the problem at hand, which is, typically, a non-trivial
accomplishment.
  In this paper we present a general Multi-Task Multi-Kernel Learning
(Multi-Task MKL) framework that subsumes well-known Multi-Task MKL
formulations, as well as several important MKL approaches on single-task
problems. We then derive a simple algorithm that can solve the unifying
framework. To demonstrate the flexibility of the proposed framework, we
formulate a new learning problem, namely Partially-Shared Common Space (PSCS)
Multi-Task MKL, and demonstrate its merits through experimentation.
An Identification System Using Eye Detection Based On Wavelets And Neural Networks
Authors: Mohamed A. El-Sayed, Mohamed A. Khafagy
Date: 2014-01-20 00:00:00
Tasks: Intrusion Detection

The randomness and uniqueness of human eye patterns is a major breakthrough
in the search for quicker, easier and highly reliable forms of automatic human
identification. It is being used extensively in security solutions. This
includes access control to physical facilities, security systems and
information databases, Suspect tracking, surveillance and intrusion detection
and by various Intelligence agencies through out the world. We use the
advantage of human eye uniqueness to identify people and approve its validity
as a biometric. . Eye detection involves first extracting the eye from a
digital face image, and then encoding the unique patterns of the eye in such a
way that they can be compared with pre-registered eye patterns. The eye
detection system consists of an automatic segmentation system that is based on
the wavelet transform, and then the Wavelet analysis is used as a pre-processor
for a back propagation neural network with conjugate gradient learning. The
inputs to the neural network are the wavelet maxima neighborhood coefficients
of face images at a particular scale. The output of the neural network is the
classification of the input into an eye or non-eye region. An accuracy of 90%
is observed for identifying test images under different conditions included in
training stage.
Study of Efficient Technique Based On 2D Tsallis Entropy For Image Thresholding
Authors: Mohamed A. El-Sayed, S. Abdel-Khalek, Eman Abdel-Aziz
Date: 2014-01-20 00:00:00
Tasks: Edge Detection, Image Segmentation, Semantic Segmentation

Thresholding is an important task in image processing. It is a main tool in
pattern recognition, image segmentation, edge detection and scene analysis. In
this paper, we present a new thresholding technique based on two-dimensional
Tsallis entropy. The two-dimensional Tsallis entropy was obtained from the
twodimensional histogram which was determined by using the gray value of the
pixels and the local average gray value of the pixels, the work it was applied
a generalized entropy formalism that represents a recent development in
statistical mechanics. The effectiveness of the proposed method is demonstrated
by using examples from the real-world and synthetic images. The performance
evaluation of the proposed technique in terms of the quality of the thresholded
images are presented. Experimental results demonstrate that the proposed method
achieve better result than the Shannon method.
A Review of Verbal and Non-Verbal Human-Robot Interactive Communication
Authors: Nikolaos Mavridis
Date: 2014-01-20 00:00:00

In this paper, an overview of human-robot interactive communication is
presented, covering verbal as well as non-verbal aspects of human-robot
interaction. Following a historical introduction, and motivation towards fluid
human-robot communication, ten desiderata are proposed, which provide an
organizational axis both of recent as well as of future research on human-robot
communication. Then, the ten desiderata are examined in detail, culminating to
a unifying discussion, and a forward-looking conclusion.
Classification of IDS Alerts with Data Mining Techniques
Authors: Hany Nashat Gabra, Ayman Mohammad Bahaa-Eldin, Huda Korashy
Date: 2014-01-20 00:00:00
Tasks: Classification, General Classification

A data mining technique to reduce the amount of false alerts within an IDS
system is proposed. The new technique achieves an accuracy of 99% compared to
97% by the current systems.
Does Syntactic Knowledge help English-Hindi SMT?
Authors: Taraka Rama, Karthik Gali, Avinesh PVS
Date: 2014-01-20 00:00:00
Tasks: Machine Translation, Translation

In this paper we explore various parameter settings of the state-of-art
Statistical Machine Translation system to improve the quality of the
translation for a `distant' language pair like English-Hindi. We proposed new
techniques for efficient reordering. A slight improvement over the baseline is
reported using these techniques. We also show that a simple pre-processing step
can improve the quality of the translation significantly.
A Genetic Algorithm to Optimize a Tweet for Retweetability
Authors: Ronald Hochreiter, Christoph Waldhauser
Date: 2014-01-20 00:00:00

Twitter is a popular microblogging platform. When users send out messages,
other users have the ability to forward these messages to their own subgraph.
Most research focuses on increasing retweetability from a node's perspective.
Here, we center on improving message style to increase the chance of a message
being forwarded. To this end, we simulate an artificial Twitter-like network
with nodes deciding deterministically on retweeting a message or not. A genetic
algorithm is used to optimize message composition, so that the reach of a
message is increased. When analyzing the algorithm's runtime behavior across a
set of different node types, we find that the algorithm consistently succeeds
in significantly improving the retweetability of a message.
An Evolutionary Approach towards Clustering Airborne Laser Scanning Data
Authors: Ronald Hochreiter, Christoph Waldhauser
Date: 2014-01-20 00:00:00
Tasks: Clustering

In land surveying, the generation of maps was greatly simplified with the
introduction of orthophotos and at a later stage with airborne LiDAR laser
scanning systems. While the original purpose of LiDAR systems was to determine
the altitude of ground elevations, newer full wave systems provide additional
information that can be used on classifying the type of ground cover and the
generation of maps. The LiDAR resulting point clouds are huge, multidimensional
data sets that need to be grouped in classes of ground cover. We propose a
genetic algorithm that aids in classifying these data sets and thus make them
usable for map generation. A key feature are tailor-made genetic operators and
fitness functions for the subject. The algorithm is compared to a traditional
k-means clustering.
Smoothed Analysis of Tensor Decompositions
Authors: Aditya Bhaskara, Moses Charikar, Ankur Moitra, Aravindan Vijayaraghavan
Date: 2013-11-14 00:00:00
Tasks: Tensor Decomposition

Low rank tensor decompositions are a powerful tool for learning generative
models, and uniqueness results give them a significant advantage over matrix
decomposition methods. However, tensors pose significant algorithmic challenges
and tensors analogs of much of the matrix algebra toolkit are unlikely to exist
because of hardness results. Efficient decomposition in the overcomplete case
(where rank exceeds dimension) is particularly challenging. We introduce a
smoothed analysis model for studying these questions and develop an efficient
algorithm for tensor decomposition in the highly overcomplete case (rank
polynomial in the dimension). In this setting, we show that our algorithm is
robust to inverse polynomial error -- a crucial property for applications in
learning since we are only allowed a polynomial number of samples. While
algorithms are known for exact tensor decomposition in some overcomplete
settings, our main contribution is in analyzing their stability in the
framework of smoothed analysis.
  Our main technical contribution is to show that tensor products of perturbed
vectors are linearly independent in a robust sense (i.e. the associated matrix
has singular values that are at least an inverse polynomial). This key result
paves the way for applying tensor methods to learning problems in the smoothed
setting. In particular, we use it to obtain results for learning multi-view
models and mixtures of axis-aligned Gaussians where there are many more
"components" than dimensions. The assumption here is that the model is not
adversarially chosen, formalized by a perturbation of model parameters. We
believe this an appealing way to analyze realistic instances of learning
problems, since this framework allows us to overcome many of the usual
limitations of using tensor methods.