pwc-archive/papers-with-abstracts huggingface dataset:
Ensembled Correlation Between Liver Analysis Outputs
Authors: Sadi Evren Seker, Y. Unal, Z. Erdem, H. Erdinc Kocer
Date: 2014-01-25 00:00:00

Data mining techniques on the biological analysis are spreading for most of
the areas including the health care and medical information. We have applied
the data mining techniques, such as KNN, SVM, MLP or decision trees over a
unique dataset, which is collected from 16,380 analysis results for a year.
Furthermore we have also used meta-classifiers to question the increased
correlation rate between the liver disorder and the liver analysis outputs. The
results show that there is a correlation among ALT, AST, Billirubin Direct and
Billirubin Total down to 15% of error rate. Also the correlation coefficient is
up to 94%. This makes possible to predict the analysis results from each other
or disease patterns can be applied over the linear correlation of the
parameters.
Category theory, logic and formal linguistics: some connections, old and new
Authors: Jean Gillibert, Christian Retoré
Date: 2014-01-25 00:00:00
Tasks: Survey

We seize the opportunity of the publication of selected papers from the
\emph{Logic, categories, semantics} workshop in the \emph{Journal of Applied
Logic} to survey some current trends in logic, namely intuitionistic and linear
type theories, that interweave categorical, geometrical and computational
considerations. We thereafter present how these rich logical frameworks can
model the way language conveys meaning.
Deverbal semantics and the Montagovian generative lexicon
Authors: Livy-Maria Real-Coelho, Christian Retoré
Date: 2014-01-25 00:00:00

We propose a lexical account of action nominals, in particular of deverbal
nominalisations, whose meaning is related to the event expressed by their base
verb. The literature about nominalisations often assumes that the semantics of
the base verb completely defines the structure of action nominals. We argue
that the information in the base verb is not sufficient to completely determine
the semantics of action nominals. We exhibit some data from different
languages, especially from Romance language, which show that nominalisations
focus on some aspects of the verb semantics. The selected aspects, however,
seem to be idiosyncratic and do not automatically result from the internal
structure of the verb nor from its interaction with the morphological suffix.
We therefore propose a partially lexicalist approach view of deverbal nouns. It
is made precise and computable by using the Montagovian Generative Lexicon, a
type theoretical framework introduced by Bassac, Mery and Retor\'e in this
journal in 2010. This extension of Montague semantics with a richer type system
easily incorporates lexical phenomena like the semantics of action nominals in
particular deverbals, including their polysemy and (in)felicitous
copredications.
Keyword and Keyphrase Extraction Using Centrality Measures on Collocation Networks
Authors: Shibamouli Lahiri, Sagnik Ray Choudhury, Cornelia Caragea
Date: 2014-01-25 00:00:00
Tasks: Clustering, Keyphrase Extraction

Keyword and keyphrase extraction is an important problem in natural language
processing, with applications ranging from summarization to semantic search to
document clustering. Graph-based approaches to keyword and keyphrase extraction
avoid the problem of acquiring a large in-domain training corpus by applying
variants of PageRank algorithm on a network of words. Although graph-based
approaches are knowledge-lean and easily adoptable in online systems, it
remains largely open whether they can benefit from centrality measures other
than PageRank. In this paper, we experiment with an array of centrality
measures on word and noun phrase collocation networks, and analyze their
performance on four benchmark datasets. Not only are there centrality measures
that perform as well as or better than PageRank, but they are much simpler
(e.g., degree, strength, and neighborhood size). Furthermore, centrality-based
methods give results that are competitive with and, in some cases, better than
two strong unsupervised baselines.
A Machine Learning Approach for the Identification of Bengali Noun-Noun Compound Multiword Expressions
Authors: Vivekananda Gayen, Kamal Sarkar
Date: 2014-01-25 00:00:00
Tasks: BIG-bench Machine Learning

This paper presents a machine learning approach for identification of Bengali
multiword expressions (MWE) which are bigram nominal compounds. Our proposed
approach has two steps: (1) candidate extraction using chunk information and
various heuristic rules and (2) training the machine learning algorithm called
Random Forest to classify the candidates into two groups: bigram nominal
compound MWE or not bigram nominal compound MWE. A variety of association
measures, syntactic and linguistic clues and a set of WordNet-based similarity
features have been used for our MWE identification task. The approach presented
in this paper can be used to identify bigram nominal compound MWE in Bengali
running text.
A Hybrid Approach to Extract Keyphrases from Medical Documents
Authors: Kamal Sarkar
Date: 2013-03-06 00:00:00
Tasks: Articles, Clustering, Keyphrase Extraction, Text Summarization

Keyphrases are the phrases, consisting of one or more words, representing the
important concepts in the articles. Keyphrases are useful for a variety of
tasks such as text summarization, automatic indexing,
clustering/classification, text mining etc. This paper presents a hybrid
approach to keyphrase extraction from medical documents. The keyphrase
extraction approach presented in this paper is an amalgamation of two methods:
the first one assigns weights to candidate keyphrases based on an effective
combination of features such as position, term frequency, inverse document
frequency and the second one assign weights to candidate keyphrases using some
knowledge about their similarities to the structure and characteristics of
keyphrases available in the memory (stored list of keyphrases). An efficient
candidate keyphrase identification method as the first component of the
proposed keyphrase extraction system has also been introduced in this paper.
The experimental results show that the proposed hybrid approach performs better
than some state-of-the art keyphrase extraction approaches.
The Stochastic Gradient Descent for the Primal L1-SVM Optimization Revisited
Authors: Constantinos Panagiotakopoulos, Petroula Tsampouka
Date: 2013-04-23 00:00:00

We reconsider the stochastic (sub)gradient approach to the unconstrained
primal L1-SVM optimization. We observe that if the learning rate is inversely
proportional to the number of steps, i.e., the number of times any training
pattern is presented to the algorithm, the update rule may be transformed into
the one of the classical perceptron with margin in which the margin threshold
increases linearly with the number of steps. Moreover, if we cycle repeatedly
through the possibly randomly permuted training set the dual variables defined
naturally via the expansion of the weight vector as a linear combination of the
patterns on which margin errors were made are shown to obey at the end of each
complete cycle automatically the box constraints arising in dual optimization.
This renders the dual Lagrangian a running lower bound on the primal objective
tending to it at the optimum and makes available an upper bound on the relative
accuracy achieved which provides a meaningful stopping criterion. In addition,
we propose a mechanism of presenting the same pattern repeatedly to the
algorithm which maintains the above properties. Finally, we give experimental
evidence that algorithms constructed along these lines exhibit a considerably
improved performance.
Identification of Protein Coding Regions in Genomic DNA Using Unsupervised FMACA Based Pattern Classifier
Authors: Pokkuluri Kiran Sree, Inampudi Ramesh Babu
Date: 2014-01-25 00:00:00

Genes carry the instructions for making proteins that are found in a cell as
a specific sequence of nucleotides that are found in DNA molecules. But, the
regions of these genes that code for proteins may occupy only a small region of
the sequence. Identifying the coding regions play a vital role in understanding
these genes. In this paper we propose a unsupervised Fuzzy Multiple Attractor
Cellular Automata (FMCA) based pattern classifier to identify the coding region
of a DNA sequence. We propose a distinct K-Means algorithm for designing FMACA
classifier which is simple, efficient and produces more accurate classifier
than that has previously been obtained for a range of different sequence
lengths. Experimental results confirm the scalability of the proposed
Unsupervised FCA based classifier to handle large volume of datasets
irrespective of the number of classes, tuples and attributes. Good
classification accuracy has been established.
Co-Multistage of Multiple Classifiers for Imbalanced Multiclass Learning
Authors: Luis Marujo, Anatole Gershman, Jaime Carbonell, David Martins de Matos, João P. Neto
Date: 2013-12-23 00:00:00
Tasks: Classification, Event Detection, General Classification, text-classification, Text Classification

In this work, we propose two stochastic architectural models (CMC and CMC-M)
with two layers of classifiers applicable to datasets with one and multiple
skewed classes. This distinction becomes important when the datasets have a
large number of classes. Therefore, we present a novel solution to imbalanced
multiclass learning with several skewed majority classes, which improves
minority classes identification. This fact is particularly important for text
classification tasks, such as event detection. Our models combined with
pre-processing sampling techniques improved the classification results on six
well-known datasets. Finally, we have also introduced a new metric SG-Mean to
overcome the multiplication by zero limitation of G-Mean.
Multimodal Transitions for Generative Stochastic Networks
Authors: Sherjil Ozair, Li Yao, Yoshua Bengio
Date: 2013-12-19 00:00:00

Generative Stochastic Networks (GSNs) have been recently introduced as an
alternative to traditional probabilistic modeling: instead of parametrizing the
data distribution directly, one parametrizes a transition operator for a Markov
chain whose stationary distribution is an estimator of the data generating
distribution. The result of training is therefore a machine that generates
samples through this Markov chain. However, the previously introduced GSN
consistency theorems suggest that in order to capture a wide class of
distributions, the transition operator in general should be multimodal,
something that has not been done before this paper. We introduce for the first
time multimodal transition distributions for GSNs, in particular using models
in the NADE family (Neural Autoregressive Density Estimator) as output
distributions of the transition operator. A NADE model is related to an RBM
(and can thus model multimodal distributions) but its likelihood (and
likelihood gradient) can be computed easily. The parameters of the NADE are
obtained as a learned function of the previous state of the learned Markov
chain. Experiments clearly illustrate the advantage of such multimodal
transition distributions over unimodal GSNs.
Automatic Detection of Calibration Grids in Time-of-Flight Images
Authors: Miles Hansard, Radu Horaud, Michel Amat, Georgios Evangelidis
Date: 2014-01-24 00:00:00

It is convenient to calibrate time-of-flight cameras by established methods,
using images of a chequerboard pattern. The low resolution of the amplitude
image, however, makes it difficult to detect the board reliably. Heuristic
detection methods, based on connected image-components, perform very poorly on
this data. An alternative, geometrically-principled method is introduced here,
based on the Hough transform. The projection of a chequerboard is represented
by two pencils of lines, which are identified as oriented clusters in the
gradient-data of the image. A projective Hough transform is applied to each of
the two clusters, in axis-aligned coordinates. The range of each transform is
properly bounded, because the corresponding gradient vectors are approximately
parallel. Each of the two transforms contains a series of collinear peaks; one
for every line in the given pencil. This pattern is easily detected, by
sweeping a dual line through the transform. The proposed Hough-based method is
compared to the standard OpenCV detection routine, by application to several
hundred time-of-flight images. It is shown that the new method detects
significantly more calibration boards, over a greater variety of poses, without
any overall loss of accuracy. This conclusion is based on an analysis of both
geometric and photometric error.
Steady-state performance of non-negative least-mean-square algorithm and its variants
Authors: Jie Chen, José Carlos M. Bermudez, Cédric Richard
Date: 2014-01-24 00:00:00

Non-negative least-mean-square (NNLMS) algorithm and its variants have been
proposed for online estimation under non-negativity constraints. The transient
behavior of the NNLMS, Normalized NNLMS, Exponential NNLMS and Sign-Sign NNLMS
algorithms have been studied in our previous work. In this technical report, we
derive closed-form expressions for the steady-state excess mean-square error
(EMSE) for the four algorithms. Simulations results illustrate the accuracy of
the theoretical results. This is a complementary material to our previous work.
Fuzzy Controller Design for Assisted Omni-Directional Treadmill Therapy
Authors: Atif Ali Khan, Oumair Naseer, Daciana Iliescu, Evor Hines
Date: 2014-01-24 00:00:00

One of the defining characteristic of human being is their ability to walk
upright. Loss or restriction of such ability whether due to the accident, spine
problem, stroke or other neurological injuries can cause tremendous stress on
the patients and hence will contribute negatively to their quality of life.
Modern research shows that physical exercise is very important for maintaining
physical fitness and adopting a healthier life style. In modern days treadmill
is widely used for physical exercises and training which enables the user to
set up an exercise regime that can be adhered to irrespective of the weather
conditions. Among the users of treadmills today are medical facilities such as
hospitals, rehabilitation centres, medical and physiotherapy clinics etc. The
process of assisted training or doing rehabilitation exercise through treadmill
is referred to as treadmill therapy. A modern treadmill is an automated machine
having built in functions and predefined features. Most of the treadmills used
today are one dimensional and user can only walk in one direction. This paper
presents the idea of using omnidirectional treadmills which will be more
appealing to the patients as they can walk in any direction, hence encouraging
them to do exercises more frequently. This paper proposes a fuzzy control
design and possible implementation strategy to assist patients in treadmill
therapy. By intelligently controlling the safety belt attached to the treadmill
user, one can help them steering left, right or in any direction. The use of
intelligent treadmill therapy can help patients to improve their walking
ability without being continuously supervised by the specialists. The patients
can walk freely within a limited space and the support system will provide
continuous evaluation of their position and can adjust the control parameters
of treadmill accordingly to provide best possible assistance.
Hypergraph Acyclicity and Propositional Model Counting
Authors: Florent Capelli, Arnaud Durand, Stefan Mengel
Date: 2014-01-24 00:00:00
Tasks: model

We show that the propositional model counting problem #SAT for CNF- formulas
with hypergraphs that allow a disjoint branches decomposition can be solved in
polynomial time. We show that this class of hypergraphs is incomparable to
hypergraphs of bounded incidence cliquewidth which were the biggest class of
hypergraphs for which #SAT was known to be solvable in polynomial time so far.
Furthermore, we present a polynomial time algorithm that computes a disjoint
branches decomposition of a given hypergraph if it exists and rejects
otherwise. Finally, we show that some slight extensions of the class of
hypergraphs with disjoint branches decompositions lead to intractable #SAT,
leaving open how to generalize the counting result of this paper.
Community Detection in Networks using Graph Distance
Authors: Sharmodeep Bhattacharyya, Peter J. Bickel
Date: 2014-01-16 00:00:00
Tasks: Community Detection

The study of networks has received increased attention recently not only from
the social sciences and statistics but also from physicists, computer
scientists and mathematicians. One of the principal problem in networks is
community detection. Many algorithms have been proposed for community finding
but most of them do not have have theoretical guarantee for sparse networks and
networks close to the phase transition boundary proposed by physicists. There
are some exceptions but all have some incomplete theoretical basis. Here we
propose an algorithm based on the graph distance of vertices in the network. We
give theoretical guarantees that our method works in identifying communities
for block models and can be extended for degree-corrected block models and
block models with the number of communities growing with number of vertices.
Despite favorable simulation results, we are not yet able to conclude that our
method is satisfactory for worst possible case. We illustrate on a network of
political blogs, Facebook networks and some other networks.
The EM algorithm and the Laplace Approximation
Authors: Niko Brümmer
Date: 2014-01-24 00:00:00

The Laplace approximation calls for the computation of second derivatives at
the likelihood maximum. When the maximum is found by the EM-algorithm, there is
a convenient way to compute these derivatives. The likelihood gradient can be
obtained from the EM-auxiliary, while the Hessian can be obtained from this
gradient with the Pearlmutter trick.
Parallel Genetic Algorithm to Solve Traveling Salesman Problem on MapReduce Framework using Hadoop Cluster
Authors: Harun Rasit Er, Nadia Erdogan
Date: 2014-01-24 00:00:00
Tasks: Combinatorial Optimization, Traveling Salesman Problem

Traveling Salesman Problem (TSP) is one of the most common studied problems
in combinatorial optimization. Given the list of cities and distances between
them, the problem is to find the shortest tour possible which visits all the
cities in list exactly once and ends in the city where it starts. Despite the
Traveling Salesman Problem is NP-Hard, a lot of methods and solutions are
proposed to the problem. One of them is Genetic Algorithm (GA). GA is a simple
but an efficient heuristic method that can be used to solve Traveling Salesman
Problem. In this paper, we will show a parallel genetic algorithm
implementation on MapReduce framework in order to solve Traveling Salesman
Problem. MapReduce is a framework used to support distributed computation on
clusters of computers. We used free licensed Hadoop implementation as MapReduce
framework.
Is Extreme Learning Machine Feasible? A Theoretical Assessment (Part II)
Authors: Shaobo Lin, Xia Liu, Jian Fang, Zongben Xu
Date: 2014-01-24 00:00:00

An extreme learning machine (ELM) can be regarded as a two stage feed-forward
neural network (FNN) learning system which randomly assigns the connections
with and within hidden neurons in the first stage and tunes the connections
with output neurons in the second stage. Therefore, ELM training is essentially
a linear learning problem, which significantly reduces the computational
burden. Numerous applications show that such a computation burden reduction
does not degrade the generalization capability. It has, however, been open that
whether this is true in theory. The aim of our work is to study the theoretical
feasibility of ELM by analyzing the pros and cons of ELM. In the previous part
on this topic, we pointed out that via appropriate selection of the activation
function, ELM does not degrade the generalization capability in the expectation
sense. In this paper, we launch the study in a different direction and show
that the randomness of ELM also leads to certain negative consequences. On one
hand, we find that the randomness causes an additional uncertainty problem of
ELM, both in approximation and learning. On the other hand, we theoretically
justify that there also exists an activation function such that the
corresponding ELM degrades the generalization capability. In particular, we
prove that the generalization capability of ELM with Gaussian kernel is
essentially worse than that of FNN with Gaussian kernel. To facilitate the use
of ELM, we also provide a remedy to such a degradation. We find that the
well-developed coefficient regularization technique can essentially improve the
generalization capability. The obtained results reveal the essential
characteristic of ELM and give theoretical guidance concerning how to use ELM.
Using Neural Network to Propose Solutions to Threats in Attack Patterns
Authors: Adetunji Adebiyi, Chris Imafidon
Date: 2014-01-24 00:00:00

In the last decade, a lot of effort has been put into securing software
application during development in the software industry. Software security is a
research field in this area which looks at how security can be weaved into
software at each phase of software development lifecycle (SDLC). The use of
attack patterns is one of the approaches that have been proposed for
integrating security during the design phase of SDLC. While this approach help
developers in identify security flaws in their software designs, the need to
apply the proper security capability that will mitigate the threat identified
is very important. To assist in this area, the uses of security patterns have
been proposed to help developers to identify solutions to recurring security
problems. However due to different types of security patterns and their
taxonomy, software developers are faced with the challenge of finding and
selecting appropriate security patterns that addresses the security risks in
their design. In this paper, we propose a tool based on Neural Network for
proposing solutions in form of security patterns to threats in attack patterns
matching attacking patterns. From the result of performance of the neural
network, we found out that the neural network was able to match attack patterns
to security patterns that can mitigate the threat in the attack pattern. With
this information developers are better informed in making decision on the
solution for securing their application.
Word-length entropies and correlations of natural language written texts
Authors: Maria Kalimeri, Vassilios Constantoudis, Constantinos Papadimitriou, Konstantinos Karamanos, Fotis K. Diakonos, Harris Papageorgiou
Date: 2014-01-24 00:00:00

We study the frequency distributions and correlations of the word lengths of
ten European languages. Our findings indicate that a) the word-length
distribution of short words quantified by the mean value and the entropy
distinguishes the Uralic (Finnish) corpus from the others, b) the tails at long
words, manifested in the high-order moments of the distributions, differentiate
the Germanic languages (except for English) from the Romanic languages and
Greek and c) the correlations between nearby word lengths measured by the
comparison of the real entropies with those of the shuffled texts are found to
be smaller in the case of Germanic and Finnish languages.