pwc-archive/papers-with-abstracts huggingface dataset:
Head Gesture Recognition using Optical Flow based Classification with Reinforcement of GMM based Background Subtraction
Authors: Parimita Saikia, Karen Das
Date: 2013-08-05 00:00:00
Tasks: General Classification, Gesture Recognition, Optical Flow Estimation

This paper describes a technique of real time head gesture recognition system. The method includes Gaussian mixture model (GMM) accompanied by optical flow algorithm which provided us the required information regarding head movement. The proposed model can be implemented in various control system. We are also presenting the result and implementation of both mentioned method.
A gradient descent technique coupled with a dynamic simulation to determine the near optimum orientation of floor plan designs
Authors: Eugénio Rodrigues, Adélio Rodrigues Gaspar, Álvaro Gomes
Date: 2013-07-25 00:00:00

A prototype tool to assist architects during the early design stage of floor
plans has been developed, consisting of an Evolutionary Program for the Space
Allocation Problem (EPSAP), which generates sets of floor plan alternatives
according to the architect's preferences; and a Floor Plan Performance
Optimization Program (FPOP), which optimizes the selected solutions according
to thermal performance criteria. The design variables subject to optimization
are window position and size, overhangs, fins, wall positioning, and building
orientation. A procedure using a transformation operator with gradient descent,
such as behavior, coupled with a dynamic simulation engine was developed for
the thermal evaluation and optimization process. However, the need to evaluate
all possible alternatives regarding designing variables being used during the
optimization process leads to an intensive use of thermal simulation, which
dramatically increases the simulation time, rendering it unpractical. An
alternative approach is a smart optimization approach, which utilizes an
oriented and adaptive search technique to efficiently find the near optimum
solution. This paper presents the search methodology for the building
orientation of floor plan designs, and the corresponding efficiency and
effectiveness indicators. The calculations are based on 100 floor plan designs
generated by EPSAP. All floor plans have the same design program, location, and
weather data, changing only their geometry. Dynamic simulation of buildings was
effectively used together with the optimization procedure in this approach to
significantly improve the designs. The use of the orientation variable has been
included in the algorithm.
Stratified Labelings for Abstract Argumentation
Authors: Matthias Thimm, Gabriele Kern-Isberner
Date: 2013-08-04 00:00:00
Tasks: Abstract Argumentation

We introduce stratified labelings as a novel semantical approach to abstract
argumentation frameworks. Compared to standard labelings, stratified labelings
provide a more fine-grained assessment of the controversiality of arguments
using ranks instead of the usual labels in, out, and undecided. We relate the
framework of stratified labelings to conditional logic and, in particular, to
the System Z ranking functions.
MonoStream: A Minimal-Hardware High Accuracy Device-free WLAN Localization System
Authors: Ibrahim Sabek, Moustafa Youssef
Date: 2013-08-04 00:00:00
Tasks: Object Recognition, Vocal Bursts Intensity Prediction

Device-free (DF) localization is an emerging technology that allows the
detection and tracking of entities that do not carry any devices nor
participate actively in the localization process. Typically, DF systems require
a large number of transmitters and receivers to achieve acceptable accuracy,
which is not available in many scenarios such as homes and small businesses. In
this paper, we introduce MonoStream as an accurate single-stream DF
localization system that leverages the rich Channel State Information (CSI) as
well as MIMO information from the physical layer to provide accurate DF
localization with only one stream. To boost its accuracy and attain low
computational requirements, MonoStream models the DF localization problem as an
object recognition problem and uses a novel set of CSI-context features and
techniques with proven accuracy and efficiency. Experimental evaluation in two
typical testbeds, with a side-by-side comparison with the state-of-the-art,
shows that MonoStream can achieve an accuracy of 0.95m with at least 26%
enhancement in median distance error using a single stream only. This
enhancement in accuracy comes with an efficient execution of less than 23ms per
location update on a typical laptop. This highlights the potential of
MonoStream usage for real-time DF tracking applications.
On estimating total time to solve SAT in distributed computing environments: Application to the SAT@home project
Authors: Alexander Semenov, Oleg Zaikin
Date: 2013-08-04 00:00:00
Tasks: Cryptanalysis, Distributed Computing

This paper proposes a method to estimate the total time required to solve SAT
in distributed environments via partitioning approach. It is based on the
observation that for some simple forms of problem partitioning one can use the
Monte Carlo approach to estimate the time required to solve an original
problem. The method proposed is based on an algorithm for searching for
partitioning with an optimal solving time estimation. We applied this method to
estimate the time required to perform logical cryptanalysis of the widely known
stream ciphers A5/1 and Bivium. The paper also describes a volunteer computing
project SAT@home aimed at solving hard combinatorial problems reduced to SAT.
In this project during several months there were solved 10 problems of logical
cryptanalysis of the A5/1 cipher thatcould not be solved using known rainbow
tables.
A Rough Computing based Performance Evaluation Approach for Educational Institutions
Authors: Debi Prasanna Acharjya, Debarati Bhattacharjee
Date: 2013-08-03 00:00:00

Performance evaluation of various organizations especially educational
institutions is a very important area of research and needs to be cultivated
more. In this paper, we propose a performance evaluation for educational
institutions using rough set on fuzzy approximation spaces with ordering rules
and information entropy. In order to measure the performance of educational
institutions, we construct an evaluation index system. Rough set on fuzzy
approximation spaces with ordering is applied to explore the evaluation index
data of each level. Furthermore, the concept of information entropy is used to
determine the weighting coefficients of evaluation indexes. Also, we find the
most important indexes that influence the weighting coefficients. The proposed
approach is validated and shows the practical viability. Moreover, the proposed
approach can be applicable to any organizations.
Universal Empathy and Ethical Bias for Artificial General Intelligence
Authors: Alexey Potapov, Sergey Rodionov
Date: 2013-08-03 00:00:00

Rational agents are usually built to maximize rewards. However, AGI agents
can find undesirable ways of maximizing any prior reward function. Therefore
value learning is crucial for safe AGI. We assume that generalized states of
the world are valuable - not rewards themselves, and propose an extension of
AIXI, in which rewards are used only to bootstrap hierarchical value learning.
The modified AIXI agent is considered in the multi-agent environment, where
other agents can be either humans or other "mature" agents, which values should
be revealed and adopted by the "infant" AGI agent. General framework for
designing such empathic agent with ethical bias is proposed also as an
extension of the universal intelligence model. Moreover, we perform experiments
in the simple Markov environment, which demonstrate feasibility of our approach
to value learning in safe AGI.
Ontology Enrichment by Extracting Hidden Assertional Knowledge from Text
Authors: Meisam Booshehri, Abbas Malekpour, Peter Luksch, Kamran Zamanifar, Shahdad Shariatmadari
Date: 2013-08-03 00:00:00
Tasks: Position, Relation Extraction

In this position paper we present a new approach for discovering some special
classes of assertional knowledge in the text by using large RDF repositories,
resulting in the extraction of new non-taxonomic ontological relations. Also we
use inductive reasoning beside our approach to make it outperform. Then, we
prepare a case study by applying our approach on sample data and illustrate the
soundness of our proposed approach. Moreover in our point of view current LOD
cloud is not a suitable base for our proposal in all informational domains.
Therefore we figure out some directions based on prior works to enrich datasets
of Linked Data by using web mining. The result of such enrichment can be reused
for further relation extraction and ontology enrichment from unstructured free
text documents.
A Comparison of Named Entity Recognition Tools Applied to Biographical Texts
Authors: Samet Atdağ, Vincent Labatut
Date: 2013-08-03 00:00:00
Tasks: Articles, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

Named entity recognition (NER) is a popular domain of natural language
processing. For this reason, many tools exist to perform this task. Amongst
other points, they differ in the processing method they rely upon, the entity
types they can detect, the nature of the text they can handle, and their
input/output formats. This makes it difficult for a user to select an
appropriate NER tool for a specific situation. In this article, we try to
answer this question in the context of biographic texts. For this matter, we
first constitute a new corpus by annotating Wikipedia articles. We then select
publicly available, well known and free for research NER tools for comparison:
Stanford NER, Illinois NET, OpenCalais NER WS and Alias-i LingPipe. We apply
them to our corpus, assess their performances and compare them. When
considering overall performances, a clear hierarchy emerges: Stanford has the
best results, followed by LingPipe, Illionois and OpenCalais. However, a more
detailed evaluation performed relatively to entity types and article categories
highlights the fact their performances are diversely influenced by those
factors. This complementarity opens an interesting perspective regarding the
combination of these individual tools in order to improve performance.
Compressive Shift Retrieval
Authors: Henrik Ohlsson, Yonina C. Eldar, Allen Y. Yang, S. Shankar Sastry
Date: 2013-03-20 00:00:00
Tasks: Compressive Sensing, Retrieval

The classical shift retrieval problem considers two signals in vector form
that are related by a shift. The problem is of great importance in many
applications and is typically solved by maximizing the cross-correlation
between the two signals. Inspired by compressive sensing, in this paper, we
seek to estimate the shift directly from compressed signals. We show that under
certain conditions, the shift can be recovered using fewer samples and less
computation compared to the classical setup. Of particular interest is shift
estimation from Fourier coefficients. We show that under rather mild conditions
only one Fourier coefficient suffices to recover the true shift.
Exploring The Contribution of Unlabeled Data in Financial Sentiment Analysis
Authors: Jimmy SJ. Ren, Wei Wang, Jiawei Wang, Stephen Shaoyi Liao
Date: 2013-08-03 00:00:00
Tasks: Classification, feature selection, General Classification, Sentiment Analysis, text-classification, Text Classification

With the proliferation of its applications in various industries, sentiment
analysis by using publicly available web data has become an active research
area in text classification during these years. It is argued by researchers
that semi-supervised learning is an effective approach to this problem since it
is capable to mitigate the manual labeling effort which is usually expensive
and time-consuming. However, there was a long-term debate on the effectiveness
of unlabeled data in text classification. This was partially caused by the fact
that many assumptions in theoretic analysis often do not hold in practice. We
argue that this problem may be further understood by adding an additional
dimension in the experiment. This allows us to address this problem in the
perspective of bias and variance in a broader view. We show that the well-known
performance degradation issue caused by unlabeled data can be reproduced as a
subset of the whole scenario. We argue that if the bias-variance trade-off is
to be better balanced by a more effective feature selection method unlabeled
data is very likely to boost the classification performance. We then propose a
feature selection framework in which labeled and unlabeled training samples are
both considered. We discuss its potential in achieving such a balance. Besides,
the application in financial sentiment analysis is chosen because it not only
exemplifies an important application, the data possesses better illustrative
power as well. The implications of this study in text classification and
financial sentiment analysis are both discussed.
A Semi-automated Statistical Algorithm for Object Separation
Authors: Madhur Srivastava, Satish K. Singh, Prasanta K. Panigrahi
Date: 2013-01-01 00:00:00
Tasks: Object

We explicate a semi-automated statistical algorithm for object identification
and segregation in both gray scale and color images. The algorithm makes
optimal use of the observation that definite objects in an image are typically
represented by pixel values having narrow Gaussian distributions about
characteristic mean values. Furthermore, for visually distinct objects, the
corresponding Gaussian distributions have negligible overlap with each other
and hence the Mahalanobis distance between these distributions are large. These
statistical facts enable one to sub-divide images into multiple thresholds of
variable sizes, each segregating similar objects. The procedure incorporates
the sensitivity of human eye to the gray pixel values into the variable
threshold size, while mapping the Gaussian distributions into localized
\delta-functions, for object separation. The effectiveness of this recursive
statistical algorithm is demonstrated using a wide variety of images.
United Statistical Algorithm, Small and Big Data: Future OF Statistician
Authors: Emanuel Parzen, Subhadeep Mukhopadhyay
Date: 2013-08-02 00:00:00
Tasks: Small Data Image Classification

This article provides the role of big idea statisticians in future of Big
Data Science. We describe the `United Statistical Algorithms' framework for
comprehensive unification of traditional and novel statistical methods for
modeling Small Data and Big Data, especially mixed data (discrete, continuous).
Finite State Machine Synthesis for Evolutionary Hardware
Authors: Andrey Bereza, Maksim Lyashov, Luis Blanco
Date: 2013-07-26 00:00:00

This article considers application of genetic algorithms for finite machine
synthesis. The resulting genetic finite state machines synthesis algorithm
allows for creation of machines with less number of states and within shorter
time. This makes it possible to use hardware-oriented genetic finite machines
synthesis algorithm in autonomous systems on reconfigurable platforms.
Analysis of Descent-Based Image Registration
Authors: Elif Vural, Pascal Frossard
Date: 2013-02-15 00:00:00
Tasks: Image Registration, Translation

We present a performance analysis for image registration with gradient
descent methods. We consider a typical multiscale registration setting where
the global 2-D translation between a pair of images is estimated by smoothing
the images and minimizing the distance between them with gradient descent. Our
study particularly concentrates on the effect of noise and low-pass filtering
on the alignment accuracy. We adopt an analytic representation for images and
analyze the well-behavedness of the image distance function by estimating the
neighborhood of translations for which it is free of undesired local minima.
This corresponds to the neighborhood of translation vectors that are correctly
computable with a simple gradient descent minimization. We show that the area
of this neighborhood increases at least quadratically with the smoothing filter
size, which justifies the use of a smoothing step in image registration with
local optimizers such as gradient descent. We then examine the effect of noise
on the alignment accuracy and derive an upper bound for the alignment error in
terms of the noise properties and filter size. Our main finding is that the
error increases at a rate that is at least linear with respect to the filter
size. Therefore, smoothing improves the well-behavedness of the distance
function; however, this comes at the cost of amplifying the alignment error in
noisy settings. Our results provide a mathematical insight about why
hierarchical techniques are effective in image registration, suggesting that
the multiscale coarse-to-fine alignment strategy of these techniques is very
suitable from the perspective of the trade-off between the well-behavedness of
the objective function and the registration accuracy. To the best of our
knowledge, this is the first such study for descent-based image registration.
Bandit Market Makers
Authors: Nicolas Della Penna, Mark D. Reid
Date: 2011-12-01 00:00:00

We introduce a modular framework for market making. It combines cost-function
based automated market makers with bandit algorithms. We obtain worst-case
profits guarantee's relative to the best in hindsight within a class of natural
"overround" cost functions . This combination allow us to have
distribution-free guarantees on the regret of profits while preserving the
bounded worst-case losses and computational tractability over combinatorial
spaces of the cost function based approach. We present simulation results to
better understand the practical behaviour of market makers from the framework.
Clustering-Based Matrix Factorization
Authors: Nima Mirbakhsh, Charles X. Ling
Date: 2013-01-28 00:00:00
Tasks: Clustering, Recommendation Systems

Recommender systems are emerging technologies that nowadays can be found in
many applications such as Amazon, Netflix, and so on. These systems help users
to find relevant information, recommendations, and their preferred items.
Slightly improvement of the accuracy of these recommenders can highly affect
the quality of recommendations. Matrix Factorization is a popular method in
Recommendation Systems showing promising results in accuracy and complexity. In
this paper we propose an extension of matrix factorization which adds general
neighborhood information on the recommendation model. Users and items are
clustered into different categories to see how these categories share
preferences. We then employ these shared interests of categories in a fusion by
Biased Matrix Factorization to achieve more accurate recommendations. This is a
complement for the current neighborhood aware matrix factorization models which
rely on using direct neighborhood information of users and items. The proposed
model is tested on two well-known recommendation system datasets: Movielens100k
and Netflix. Our experiment shows applying the general latent features of
categories into factorized recommender models improves the accuracy of
recommendations. The current neighborhood-aware models need a great number of
neighbors to acheive good accuracies. To the best of our knowledge, the
proposed model is better than or comparable with the current neighborhood-aware
models when they consider fewer number of neighbors.
Hybrid Focal Stereo Networks for Pattern Analysis in Homogeneous Scenes
Authors: Emanuel Aldea, Khurom H. Kiyani
Date: 2013-08-01 00:00:00
Tasks: Camera Calibration

In this paper we address the problem of multiple camera calibration in the
presence of a homogeneous scene, and without the possibility of employing
calibration object based methods. The proposed solution exploits salient
features present in a larger field of view, but instead of employing active
vision we replace the cameras with stereo rigs featuring a long focal analysis
camera, as well as a short focal registration camera. Thus, we are able to
propose an accurate solution which does not require intrinsic variation models
as in the case of zooming cameras. Moreover, the availability of the two views
simultaneously in each rig allows for pose re-estimation between rigs as often
as necessary. The algorithm has been successfully validated in an indoor
setting, as well as on a difficult scene featuring a highly dense pilgrim crowd
in Makkah.
Design and Development of an Expert System to Help Head of University Departments
Authors: Shervan Fekri-Ershad, Hadi Tajalizadeh, Shahram Jafari
Date: 2013-08-01 00:00:00

One of the basic tasks which is responded for head of each university
department, is employing lecturers based on some default factors such as
experience, evidences, qualifies and etc. In this respect, to help the heads,
some automatic systems have been proposed until now using machine learning
methods, decision support systems (DSS) and etc. According to advantages and
disadvantages of the previous methods, a full automatic system is designed in
this paper using expert systems. The proposed system is included two main
steps. In the first one, the human expert's knowledge is designed as decision
trees. The second step is included an expert system which is evaluated using
extracted rules of these decision trees. Also, to improve the quality of the
proposed system, a majority voting algorithm is proposed as post processing
step to choose the best lecturer which satisfied more expert's decision trees
for each course. The results are shown that the designed system average
accuracy is 78.88. Low computational complexity, simplicity to program and are
some of other advantages of the proposed system.
MAS for video objects segmentation and tracking based on active contours and SURF descriptor
Authors: Mohamed Chakroun, Ali Wali, Adel M. ALIMI
Date: 2013-08-01 00:00:00
Tasks: Segmentation, Video Segmentation, Video Semantic Segmentation

In computer vision, video segmentation and tracking is an important
challenging issue. In this paper, we describe a new video sequences
segmentation and tracking algorithm based on MAS "multi-agent systems" and SURF
"Speeded Up Robust Features". Our approach consists in modelling a multi-agent
system for segmenting the first image from a video sequence and tracking
objects in the video sequences. The used agents are supervisor and explorator
agents, they are communicating between them and they inspire in their behavior
from active contours approaches. The tracking of objects is based on SURF
descriptors "Speed Up Robust Features". We used the DIMA platform and "API
Ateji PX" (an extension of the Java language to facilitate parallel programming
on heterogeneous architectures) to implement this algorithm. The experimental
results indicate that the proposed algorithm is more robust and faster than
previous approaches.
Exact and Heuristic Methods for the Assembly Line Worker Assignment and Balancing Problem
Authors: Leonardo Borba, Marcus Ritt
Date: 2013-08-01 00:00:00
Tasks: valid

In traditional assembly lines, it is reasonable to assume that task execution
times are the same for each worker. However, in sheltered work centres for
disabled this assumption is not valid: some workers may execute some tasks
considerably slower or even be incapable of executing them. Worker
heterogeneity leads to a problem called the assembly line worker assignment and
balancing problem (ALWABP). For a fixed number of workers the problem is to
maximize the production rate of an assembly line by assigning workers to
stations and tasks to workers, while satisfying precedence constraints between
the tasks. This paper introduces new heuristic and exact methods to solve this
problem. We present a new MIP model, propose a novel heuristic algorithm based
on beam search, as well as a task-oriented branch-and-bound procedure which
uses new reduction rules and lower bounds for solving the problem. Extensive
computational tests on a large set of instances show that these methods are
effective and improve over existing ones.