pwc-archive/papers-with-abstracts huggingface dataset:
Less is More: Building Selective Anomaly Ensembles
Authors: Shebuti Rayana, Leman Akoglu
Date: 2015-01-08 00:00:00
Tasks: Clustering, Event Detection

Ensemble techniques for classification and clustering have long proven
effective, yet anomaly ensembles have been barely studied. In this work, we tap
into this gap and propose a new ensemble approach for anomaly mining, with
application to event detection in temporal graphs. Our method aims to combine
results from heterogeneous detectors with varying outputs, and leverage the
evidence from multiple sources to yield better performance. However, trusting
all the results may deteriorate the overall ensemble accuracy, as some
detectors may fall short and provide inaccurate results depending on the nature
of the data in hand. This suggests that being selective in which results to
combine is vital in building effective ensembles---hence "less is more".
  In this paper we propose SELECT; an ensemble approach for anomaly mining that
employs novel techniques to automatically and systematically select the results
to assemble in a fully unsupervised fashion. We apply our method to event
detection in temporal graphs, where SELECT successfully utilizes five base
detectors and seven consensus methods under a unified ensemble framework. We
provide extensive quantitative evaluation of our approach on five real-world
datasets (four with ground truth), including Enron email communications, New
York Times news corpus, and World Cup 2014 Twitter news feed. Thanks to its
selection mechanism, SELECT yields superior performance compared to individual
detectors alone, the full ensemble (naively combining all results), and an
existing diversity-based ensemble.
Quantifying Scripts: Defining metrics of characters for quantitative and descriptive analysis
Authors: Vinodh Rajan
Date: 2015-01-08 00:00:00
Tasks: Descriptive

Analysis of scripts plays an important role in paleography and in
quantitative linguistics. Especially in the field of digital paleography
quantitative features are much needed to differentiate glyphs. We describe an
elaborate set of metrics that quantify qualitative information contained in
characters and hence indirectly also quantify the scribal features. We broadly
divide the metrics into several categories and describe each individual metric
with its underlying qualitative significance. The metrics are largely derived
from the related area of gesture design and recognition. We also propose
several novel metrics. The proposed metrics are soundly grounded on the
principles of handwriting production and handwriting analysis. These computed
metrics could serve as descriptors for scripts and also be used for comparing
and analyzing scripts. We illustrate some quantitative analysis based on the
proposed metrics by applying it to the paleographic evolution of the medieval
Tamil script from Brahmi. We also outline future work.
The Hebrew Bible as Data: Laboratory - Sharing - Experiences
Authors: Dirk Roorda
Date: 2015-01-08 00:00:00

The systematic study of ancient texts including their production,
transmission and interpretation is greatly aided by the digital methods that
started taking off in the 1970s. But how is that research in turn transmitted
to new generations of researchers? We tell a story of Bible and computer across
the decades and then point out the current challenges: (1) finding a stable
data representation for changing methods of computation; (2) sharing results in
inter- and intra-disciplinary ways, for reproducibility and
cross-fertilization. We report recent developments in meeting these challenges.
The scene is the text database of the Hebrew Bible, constructed by the Eep
Talstra Centre for Bible and Computer (ETCBC), which is still growing in detail
and sophistication. We show how a subtle mix of computational ingredients
enable scholars to research the transmission and interpretation of the Hebrew
Bible in new ways: (1) a standard data format, Linguistic Annotation Framework
(LAF); (2) the methods of scientific computing, made accessible by
(interactive) Python and its associated ecosystem. Additionally, we show how
these efforts have culminated in the construction of a new, publicly accessible
search engine SHEBANQ, where the text of the Hebrew Bible and its underlying
data can be queried in a simple, yet powerful query language MQL, and where
those queries can be saved and shared.
Optimal Radiometric Calibration for Camera-Display Communication
Authors: Wenjia Yuan, Eric Wengrowski, Kristin J. Dana, Ashwin Ashok, Marco Gruteser, Narayan Mandayam
Date: 2015-01-08 00:00:00

We present a novel method for communicating between a camera and display by
embedding and recovering hidden and dynamic information within a displayed
image. A handheld camera pointed at the display can receive not only the
display image, but also the underlying message. These active scenes are
fundamentally different from traditional passive scenes like QR codes because
image formation is based on display emittance, not surface reflectance.
Detecting and decoding the message requires careful photometric modeling for
computational message recovery. Unlike standard watermarking and steganography
methods that lie outside the domain of computer vision, our message recovery
algorithm uses illumination to optically communicate hidden messages in real
world scenes. The key innovation of our approach is an algorithm that performs
simultaneous radiometric calibration and message recovery in one convex
optimization problem. By modeling the photometry of the system using a
camera-display transfer function (CDTF), we derive a physics-based kernel
function for support vector machine classification. We demonstrate that our
method of optimal online radiometric calibration (OORC) leads to an efficient
and robust algorithm for computational messaging between nine commercial
cameras and displays.
An Effective Image Feature Classiffication using an improved SOM
Authors: M. Abdelsamea, Marghny H. Mohamed, Mohamed Bamatraf
Date: 2015-01-08 00:00:00
Tasks: General Classification

Image feature classification is a challenging problem in many computer vision
applications, specifically, in the fields of remote sensing, image analysis and
pattern recognition. In this paper, a novel Self Organizing Map, termed
improved SOM (iSOM), is proposed with the aim of effectively classifying
Mammographic images based on their texture feature representation. The main
contribution of the iSOM is to introduce a new node structure for the map
representation and adopting a learning technique based on Kohonen SOM
accordingly. The main idea is to control, in an unsupervised fashion, the
weight updating procedure depending on the class reliability of the node,
during the weight update time. Experiments held on a real Mammographic images.
Results showed high accuracy compared to classical SOM and other state-of-art
classifiers.
Sparse Solutions to Nonnegative Linear Systems and Applications
Authors: Aditya Bhaskara, Ananda Theertha Suresh, Morteza Zadimoghaddam
Date: 2015-01-07 00:00:00

We give an efficient algorithm for finding sparse approximate solutions to
linear systems of equations with nonnegative coefficients. Unlike most known
results for sparse recovery, we do not require {\em any} assumption on the
matrix other than non-negativity. Our algorithm is combinatorial in nature,
inspired by techniques for the set cover problem, as well as the multiplicative
weight update method.
  We then present a natural application to learning mixture models in the PAC
framework. For learning a mixture of $k$ axis-aligned Gaussians in $d$
dimensions, we give an algorithm that outputs a mixture of $O(k/\epsilon^3)$
Gaussians that is $\epsilon$-close in statistical distance to the true
distribution, without any separation assumptions. The time and sample
complexity is roughly $O(kd/\epsilon^3)^{d}$. This is polynomial when $d$ is
constant -- precisely the regime in which known methods fail to identify the
components efficiently.
  Given that non-negativity is a natural assumption, we believe that our result
may find use in other settings in which we wish to approximately explain data
using a small number of a (large) candidate set of components.
An Introduction to Matrix Concentration Inequalities
Authors: Joel A. Tropp
Date: 2015-01-07 00:00:00

In recent years, random matrices have come to play a major role in
computational mathematics, but most of the classical areas of random matrix
theory remain the province of experts. Over the last decade, with the advent of
matrix concentration inequalities, research has advanced to the point where we
can conquer many (formerly) challenging problems with a page or two of
arithmetic. The aim of this monograph is to describe the most successful
methods from this area along with some interesting examples that these
techniques can illuminate.
Scaling laws in human speech, decreasing emergence of new words and a generalized model
Authors: Ruokuang Lin, Qianli D. Y. Ma, Chunhua Bian
Date: 2014-12-16 00:00:00

Human language, as a typical complex system, its organization and evolution
is an attractive topic for both physical and cultural researchers. In this
paper, we present the first exhaustive analysis of the text organization of
human speech. Two important results are that: (i) the construction and
organization of spoken language can be characterized as Zipf's law and Heaps'
law, as observed in written texts; (ii) word frequency vs. rank distribution
and the growth of distinct words with the increase of text length shows
significant differences between book and speech. In speech word frequency
distribution are more concentrated on higher frequency words, and the emergence
of new words decreases much rapidly when the content length grows. Based on
these observations, a new generalized model is proposed to explain these
complex dynamical behaviors and the differences between speech and book.
Autonomous Fault Detection in Self-Healing Systems using Restricted Boltzmann Machines
Authors: Chris Schneider, Adam Barker, Simon Dobson
Date: 2015-01-07 00:00:00
Tasks: Fault Detection

Autonomously detecting and recovering from faults is one approach for
reducing the operational complexity and costs associated with managing
computing environments. We present a novel methodology for autonomously
generating investigation leads that help identify systems faults, and extends
our previous work in this area by leveraging Restricted Boltzmann Machines
(RBMs) and contrastive divergence learning to analyse changes in historical
feature data. This allows us to heuristically identify the root cause of a
fault, and demonstrate an improvement to the state of the art by showing
feature data can be predicted heuristically beyond a single instance to include
entire sequences of information.
Median evidential c-means algorithm and its application to community detection
Authors: Kuang Zhou, Arnaud Martin, Quan Pan, Zhun-Ga Liu
Date: 2015-01-07 00:00:00
Tasks: Clustering, Community Detection, Graph Clustering, Prototype Selection

Median clustering is of great value for partitioning relational data. In this
paper, a new prototype-based clustering method, called Median Evidential
C-Means (MECM), which is an extension of median c-means and median fuzzy
c-means on the theoretical framework of belief functions is proposed. The
median variant relaxes the restriction of a metric space embedding for the
objects but constrains the prototypes to be in the original data set. Due to
these properties, MECM could be applied to graph clustering problems. A
community detection scheme for social networks based on MECM is investigated
and the obtained credal partitions of graphs, which are more refined than crisp
and fuzzy ones, enable us to have a better understanding of the graph
structures. An initial prototype-selection scheme based on evidential
semi-centrality is presented to avoid local premature convergence and an
evidential modularity function is defined to choose the optimal number of
communities. Finally, experiments in synthetic and real data sets illustrate
the performance of MECM and show its difference to other methods.
Comparison of Selection Methods in On-line Distributed Evolutionary Robotics
Authors: Iñaki Fernández Pérez, Amine Boumaza, François Charpillet
Date: 2015-01-07 00:00:00
Tasks: Evolutionary Algorithms, Robot Navigation

In this paper, we study the impact of selection methods in the context of
on-line on-board distributed evolutionary algorithms. We propose a variant of
the mEDEA algorithm in which we add a selection operator, and we apply it in a
taskdriven scenario. We evaluate four selection methods that induce different
intensity of selection pressure in a multi-robot navigation with obstacle
avoidance task and a collective foraging task. Experiments show that a small
intensity of selection pressure is sufficient to rapidly obtain good
performances on the tasks at hand. We introduce different measures to compare
the selection methods, and show that the higher the selection pressure, the
better the performances obtained, especially for the more challenging food
foraging task.
The SP theory of intelligence: an overview
Authors: J. Gerard Wolff
Date: 2013-06-13 00:00:00
Tasks: Retrieval

This article is an overview of the "SP theory of intelligence". The theory
aims to simplify and integrate concepts across artificial intelligence,
mainstream computing and human perception and cognition, with information
compression as a unifying theme. It is conceived as a brain-like system that
receives 'New' information and stores some or all of it in compressed form as
'Old' information. It is realised in the form of a computer model -- a first
version of the SP machine. The concept of "multiple alignment" is a powerful
central idea. Using heuristic techniques, the system builds multiple alignments
that are 'good' in terms of information compression. For each multiple
alignment, probabilities may be calculated. These provide the basis for
calculating the probabilities of inferences. The system learns new structures
from partial matches between patterns. Using heuristic techniques, the system
searches for sets of structures that are 'good' in terms of information
compression. These are normally ones that people judge to be 'natural', in
accordance with the 'DONSVIC' principle -- the discovery of natural structures
via information compression. The SP theory may be applied in several areas
including 'computing', aspects of mathematics and logic, representation of
knowledge, natural language processing, pattern recognition, several kinds of
reasoning, information storage and retrieval, planning and problem solving,
information compression, neuroscience, and human perception and cognition.
Examples include the parsing and production of language including discontinuous
dependencies in syntax, pattern recognition at multiple levels of abstraction
and its integration with part-whole relations, nonmonotonic reasoning and
reasoning with default values, reasoning in Bayesian networks including
'explaining away', causal diagnosis, and the solving of a geometric analogy
problem.
Evidential-EM Algorithm Applied to Progressively Censored Observations
Authors: Kuang Zhou, Arnaud Martin, Quan Pan
Date: 2015-01-07 00:00:00

Evidential-EM (E2M) algorithm is an effective approach for computing maximum
likelihood estimations under finite mixture models, especially when there is
uncertain information about data. In this paper we present an extension of the
E2M method in a particular case of incom-plete data, where the loss of
information is due to both mixture models and censored observations. The prior
uncertain information is expressed by belief functions, while the
pseudo-likelihood function is derived based on imprecise observations and prior
knowledge. Then E2M method is evoked to maximize the generalized likelihood
function to obtain the optimal estimation of parameters. Numerical examples
show that the proposed method could effectively integrate the uncertain prior
infor-mation with the current imprecise knowledge conveyed by the observed
data.
Roman Urdu Opinion Mining System (RUOMiS)
Authors: Misbah Daud, Rafiullah Khan, Mohibullah, Aitazaz Daud
Date: 2015-01-07 00:00:00
Tasks: Opinion Mining

Convincing a customer is always considered as a challenging task in every
business. But when it comes to online business, this task becomes even more
difficult. Online retailers try everything possible to gain the trust of the
customer. One of the solutions is to provide an area for existing users to
leave their comments. This service can effectively develop the trust of the
customer however normally the customer comments about the product in their
native language using Roman script. If there are hundreds of comments this
makes difficulty even for the native customers to make a buying decision. This
research proposes a system which extracts the comments posted in Roman Urdu,
translate them, find their polarity and then gives us the rating of the
product. This rating will help the native and non-native customers to make
buying decision efficiently from the comments posted in Roman Urdu.
Leader Follower Formation Control of Ground Vehicles Using Camshift Based Guidance
Authors: S. M. Vaitheeswaran, Bharath M. K., Gokul M
Date: 2015-01-07 00:00:00

Autonomous ground vehicles have been designed for the purpose of that relies
on ranging and bearing information received from forward looking camera on the
Formation control . A visual guidance control algorithm is designed where real
time image processing is used to provide feedback signals. The vision subsystem
and control subsystem work in parallel to accomplish formation control. A
proportional navigation and line of sight guidance laws are used to estimate
the range and bearing information from the leader vehicle using the vision
subsystem. The algorithms for vision detection and localization used here are
similar to approaches for many computer vision tasks such as face tracking and
detection that are based color-and texture based features, and non-parametric
Continuously Adaptive Mean-shift algorithms to keep track of the leader. This
is being proposed for the first time in the leader follower framework. The
algorithms are simple but effective for real time and provide an alternate
approach to traditional based approaches like the Viola Jones algorithm.
Further to stabilize the follower to the leader trajectory, the sliding mode
controller is used to dynamically track the leader. The performance of the
results is demonstrated in simulation and in practical experiments.
Deep Autoencoders for Dimensionality Reduction of High-Content Screening Data
Authors: Lee Zamparo, Zhaolei Zhang
Date: 2015-01-07 00:00:00
Tasks: Clustering, Dimensionality Reduction, Vocal Bursts Intensity Prediction

High-content screening uses large collections of unlabeled cell image data to
reason about genetics or cell biology. Two important tasks are to identify
those cells which bear interesting phenotypes, and to identify sub-populations
enriched for these phenotypes. This exploratory data analysis usually involves
dimensionality reduction followed by clustering, in the hope that clusters
represent a phenotype. We propose the use of stacked de-noising auto-encoders
to perform dimensionality reduction for high-content screening. We demonstrate
the superior performance of our approach over PCA, Local Linear Embedding,
Kernel PCA and Isomap.
Reconstructing subclonal composition and evolution from whole genome sequencing of tumors
Authors: Amit G. Deshwar, Shankar Vembu, Christina K. Yung, Gun Ho Jang, Lincoln Stein, Quaid Morris
Date: 2014-06-27 00:00:00

Tumors often contain multiple subpopulations of cancerous cells defined by
distinct somatic mutations. We describe a new method, PhyloWGS, that can be
applied to WGS data from one or more tumor samples to reconstruct complete
genotypes of these subpopulations based on variant allele frequencies (VAFs) of
point mutations and population frequencies of structural variations. We
introduce a principled phylogenic correction for VAFs in loci affected by copy
number alterations and we show that this correction greatly improves subclonal
reconstruction compared to existing methods.
ITCM: A Real Time Internet Traffic Classifier Monitor
Authors: Silas Santiago Lopes Pereira, José Everardo Bessa Maia, Jorge Luiz de Castro e Silva
Date: 2015-01-06 00:00:00
Tasks: Classification, Ensemble Learning, General Classification, Traffic Classification

The continual growth of high speed networks is a challenge for real-time
network analysis systems. The real time traffic classification is an issue for
corporations and ISPs (Internet Service Providers). This work presents the
design and implementation of a real time flow-based network traffic
classification system. The classifier monitor acts as a pipeline consisting of
three modules: packet capture and pre-processing, flow reassembly, and
classification with Machine Learning (ML). The modules are built as concurrent
processes with well defined data interfaces between them so that any module can
be improved and updated independently. In this pipeline, the flow reassembly
function becomes the bottleneck of the performance. In this implementation, was
used a efficient method of reassembly which results in a average delivery delay
of 0.49 seconds, approximately. For the classification module, the performances
of the K-Nearest Neighbor (KNN), C4.5 Decision Tree, Naive Bayes (NB), Flexible
Naive Bayes (FNB) and AdaBoost Ensemble Learning Algorithm are compared in
order to validate our approach.
Arabic Text Categorization Algorithm using Vector Evaluation Method
Authors: Ashraf Odeh, Aymen Abu-Errub, Qusai Shambour, Nidal Turab
Date: 2015-01-06 00:00:00
Tasks: General Classification, Information Retrieval, Retrieval, Text Categorization

Text categorization is the process of grouping documents into categories
based on their contents. This process is important to make information
retrieval easier, and it became more important due to the huge textual
information available online. The main problem in text categorization is how to
improve the classification accuracy. Although Arabic text categorization is a
new promising field, there are a few researches in this field. This paper
proposes a new method for Arabic text categorization using vector evaluation.
The proposed method uses a categorized Arabic documents corpus, and then the
weights of the tested document's words are calculated to determine the document
keywords which will be compared with the keywords of the corpus categorizes to
determine the tested document's best category.
Unknown Words Analysis in POS tagging of Sinhala Language
Authors: A. J. P. M. P. Jayaweera, N. G. J. Dias
Date: 2015-01-06 00:00:00
Tasks: POS, POS Tagging

Part of Speech (POS) is a very vital topic in Natural Language Processing
(NLP) task in any language, which involves analysing the construction of the
language, behaviours and the dynamics of the language, the knowledge that could
be utilized in computational linguistics analysis and automation applications.
In this context, dealing with unknown words (words do not appear in the lexicon
referred as unknown words) is also an important task, since growing NLP systems
are used in more and more new applications. One aid of predicting lexical
categories of unknown words is the use of syntactical knowledge of the
language. The distinction between open class words and closed class words
together with syntactical features of the language used in this research to
predict lexical categories of unknown words in the tagging process. An
experiment is performed to investigate the ability of the approach to parse
unknown words using syntactical knowledge without human intervention. This
experiment shows that the performance of the tagging process is enhanced when
word class distinction is used together with syntactic rules to parse sentences
containing unknown words in Sinhala language.