pwc-archive/papers-with-abstracts huggingface dataset:
An effective approach for classification of advanced malware with high accuracy
Authors: Sharma Ashu, Sahay Sanjay K.
Date: 2016-06-22 00:00:00
Tasks: General Classification, Vocal Bursts Intensity Prediction

Combating malware is very important for software/systems security, but to
prevent the software/systems from the advanced malware, viz. metamorphic
malware is a challenging task, as it changes the structure/code after each
infection. Therefore in this paper, we present a novel approach to detect the
advanced malware with high accuracy by analyzing the occurrence of opcodes
(features) by grouping the executables. These groups are made on the basis of
our earlier studies [1] that the difference between the sizes of any two
malware generated by popular advanced malware kits viz. PS-MPC, G2 and NGVCK
are within 5 KB. On the basis of obtained promising features, we studied the
performance of thirteen classifiers using N-fold cross-validation available in
machine learning tool WEKA. Among these thirteen classifiers we studied
in-depth top five classifiers (Random forest, LMT, NBT, J48 and FT) and obtain
more than 96.28% accuracy for the detection of unknown malware, which is better
than the maximum detection accuracy (95.9%) reported by Santos et al (2013). In
these top five classifiers, our approach obtained a detection accuracy of
97.95% by the Random forest.
Grouping the executables to detect malware with high accuracy
Authors: Sahay Sanjay K., Sharma Ashu
Date: 2016-06-22 00:00:00
Tasks: Clustering, Vocal Bursts Intensity Prediction

The metamorphic malware variants with the same malicious behavior (family),
can obfuscate themselves to look different from each other. This variation in
structure leads to a huge signature database for traditional signature matching
techniques to detect them. In order to effective and efficient detection of
malware in large amounts of executables, we need to partition these files into
groups which can identify their respective families. In addition, the grouping
criteria should be chosen such a way that, it can also be applied to unknown
files encounter on computers for classification. This paper discusses the study
of malware and benign executables in groups to detect unknown malware with high
accuracy. We studied sizes of malware generated by three popular second
generation malware (metamorphic malware) creator kits viz. G2, PS-MPC and
NGVCK, and observed that the size variation in any two generated malware from
same kit is not much. Hence, we grouped the executables on the basis of malware
sizes by using Optimal k-Means Clustering algorithm and used these obtained
groups to select promising features for training (Random forest, J48, LMT, FT
and NBT) classifiers to detect variants of malware or unknown malware. We find
that detection of malware on the basis of their respected file sizes gives
accuracy up to 99.11% from the classifiers.
A New Currency of the Future: The Novel Commodity Money with Attenuation Coefficient Based on the Logistics Cost of Anchor
Date: 2016-06-22 00:00:00

In this paper, we reveal the attenuation mechanism of anchor of the commodity
money from the perspective of logistics warehousing costs, and propose a novel
Decayed Commodity Money (DCM) for the store of value across time and space.
Considering the logistics cost of commodity warehousing by the third financial
institution such as London Metal Exchange, we can award the difference between
the original and the residual value of the anchor to the financial institution.
This type of currency has the characteristic of self-decaying value over time.
Therefore DCM has the advantages of both the commodity money which has the
function of preserving wealth and credit currency without the logistics cost.
In addition, DCM can also avoid the defects that precious metal money is
hoarded by market and credit currency often leads to excessive liquidity. DCM
is also different from virtual currency, such as bitcoin, which does not have a
corresponding commodity anchor. As a conclusion, DCM can provide a new way of
storing wealth for nations, corporations and individuals effectively.
Autoantibody recognition mechanisms of MUC1
Date: 2016-06-21 00:00:00

The most cost-effective blood-based, noninvasive molecular cancer biomarkers
are based on p53 epitopes and MUC1 tandem repeats. Here we use dimensionally
compressed bioinformatic fractal scaling analysis to compare the two distinct
and comparable probes, which examine different sections of the autoantibody
population, achieving combined sensitivities of order 50%. We discover a
promising MUC1 epitope in the SEA region outside the tandem repeats.
Sparse convolutional coding for neuronal ensemble identification
Date: 2016-06-22 00:00:00

Cell ensembles, originally proposed by Donald Hebb in 1949, are subsets of
synchronously firing neurons and proposed to explain basic firing behavior in
the brain. Despite having been studied for many years no conclusive evidence
has been presented yet for their existence and involvement in information
processing such that their identification is still a topic of modern research,
especially since simultaneous recordings of large neuronal population have
become possible in the past three decades. These large recordings pose a
challenge for methods allowing to identify individual neurons forming cell
ensembles and their time course of activity inside the vast amounts of spikes
recorded. Related work so far focused on the identification of purely simulta-
neously firing neurons using techniques such as Principal Component Analysis.
In this paper we propose a new algorithm based on sparse convolution coding
which is also able to find ensembles with temporal structure. Application of
our algorithm to synthetically generated datasets shows that it outperforms
previous work and is able to accurately identify temporal cell ensembles even
when those contain overlapping neurons or when strong background noise is
present.
Distinguishing Convergence on Phylogenetic Networks
Date: 2016-06-23 00:00:00

We compare the phylogenetic tensors for various trees and networks for two,
three and four taxa. If the probability spaces between one tree or network and
another are not identical then there will be phylogenetic tensors that could
have arisen on one but not the other. We call these two trees or networks
distinguishable from each other. We show that for the binary symmetric model
there are no two-taxon trees and networks that are distinguishable from each
other, however there are three-taxon trees and networks that are
distinguishable from each other.
  We compare the time parameters for the phylogenetic tensors for various taxon
label permutations on a given tree or network. If the time parameters on one
taxon label permutation in terms of the other taxon label permutation are all
non-negative then we say that the two taxon label permutations are not network
identifiable from each other. We show that some taxon label permutations are
network identifiable from each other.
  We show that some four-taxon networks satisfy the four-point condition. Of
the two "shapes" of four-taxon rooted trees, one is defined by the cluster,
b,c,d, labelling taxa alphabetically from left to right. The network with this
shape and convergence between the two taxa with the root as their most recent
common ancestor satisfies the four-point condition.
  The phylogenetic tensors contain polynomial equations that cannot be easily
solved for four-taxon or higher trees or networks. We show how methods from
algebraic geometry, such as Gr\"obner bases, can be used to solve the
polynomial equations. We show that some four-taxon trees and networks can be
distinguished from each other.
The "Hard Problem" of Life
Date: 2016-06-23 00:00:00

Chalmer's famously identified pinpointing an explanation for our subjective
experience as the "hard problem of consciousness". He argued that subjective
experience constitutes a "hard problem" in the sense that its explanation will
ultimately require new physical laws or principles. Here, we propose a
corresponding "hard problem of life" as the problem of how `information' can
affect the world. In this essay we motivate both why the problem of information
as a causal agent is central to explaining life, and why it is hard - that is,
why we suspect that a full resolution of the hard problem of life will, similar
to as has been proposed for the hard problem of consciousness, ultimately not
be reducible to known physical principles.
Selective Term Proximity Scoring Via BP-ANN
Authors: Yang Ju, Tong Jiancong, Stones Rebecca J., Zhang Zhaohua, Ye Benjun, Wang Gang, Liu Xiaoguang
Date: 2016-06-23 00:00:00
Tasks: Document Ranking

When two terms occur together in a document, the probability of a close
relationship between them and the document itself is greater if they are in
nearby positions. However, ranking functions including term proximity (TP)
require larger indexes than traditional document-level indexing, which slows
down query processing. Previous studies also show that this technique is not
effective for all types of queries. Here we propose a document ranking model
which decides for which queries it would be beneficial to use a proximity-based
ranking, based on a collection of features of the query. We use a machine
learning approach in determining whether utilizing TP will be beneficial.
Experiments show that the proposed model returns improved rankings while also
reducing the overhead incurred as a result of using TP statistics.
Packaging stiff polymers in small containers: A molecular dynamics study
Date: 2016-09-23 00:00:00

The question of how stiff polymers are able to pack into small containers is
particularly relevant to the study of DNA packaging in viruses. A reduced
version of the problem based on coarse-grained representations of the main
components of the system -- the DNA polymer and the spherical viral capsid --
has been studied by molecular dynamics simulation. The results, involving
longer polymers than in earlier work, show that as polymers become more rigid
there is an increasing tendency to self-organize as spools that wrap from the
inside out, rather than the inverse direction seen previously. In the final
state, a substantial part of the polymer is packed into one or more coaxial
spools, concentrically layered with different orientations, a form of packaging
achievable without twisting the polymer.
Stochastic models of the binding kinetics of VEGF-A to VEGFR1 and VEGFR2 in endothelial cells
Date: 2016-06-23 00:00:00

Vascular endothelial growth factor receptors (VEGFRs) are receptor tyrosine
kinases (RTKs) that regulate proliferation, migration, angiogenesis and
vascular permeability of endothelial cells. VEGFR1 and VEGFR2 bind vascular
endothelial growth factors (VEGFs), inducing receptor dimerisation and
activation, characterised by phosphorylation of tyrosine residues in their
cytoplasmic domain. Although experimental evidence suggests that RTK signalling
occurs both on the plasma membrane and intra-cellularly, and reveals the role
of endocytosis in RTK signal transduction, we still lack knowledge of VEGFR
phosphorylation-site use and of the spatiotemporal regulation of VEGFR
signalling. In this paper, we introduce four stochastic mathematical models to
study the binding kinetics of vascular endothelial growth factor VEGF-A to
VEGFR1 and VEGFR2, and phosphorylation. The formation of phosphorylated dimers
on the cell surface is a two-step process: diffusive transport and binding. The
first two of our models only consider VEGFR2, which allows us to introduce new
stochastic descriptors making use of a matrix-analytic approach. The two
remaining models describe the competition of VEGFR1 and VEGFR2 for ligand
availability, and are analysed making use of Gillespie simulations and the van
Kampen approximation. Under the hypothesis that bound phosphorylated receptor
dimers are the signalling units, we study the time to reach a threshold number
of such complexes. Our results indicate that the presence of VEGFR1 does not
only affect the timescale to reach a given signalling threshold, but it also
affects the maximum attainable threshold. This result is consistent with the
conjectured role of VEGFR1 as a decoy receptor, that prevents VEGF-A binding to
VEGFR2, and thus, VEGFR2 attaining suitable phosphorylation levels. We identify
an optimum range of ligand concentration for sustained dimer phosphorylation.
Skorohod's representation theorem and optimal strategies for markets with frictions
Date: 2017-04-12 00:00:00

We prove the existence of optimal strategies for agents with cumulative
prospect theory preferences who trade in a continuous-time illiquid market,
transcending known results which pertained only to risk-averse utility
maximizers. The arguments exploit an extension of Skorohod's representation
theorem for tight sequences of probability measures. This method is applicable
in a number of similar optimization problems.
In the Light of Deep Coalescence: Revisiting Trees Within Networks
Date: 2016-06-23 00:00:00

Phylogenetic networks model reticulate evolutionary histories. The last two
decades have seen an increased interest in establishing mathematical results
and developing computational methods for inferring and analyzing these
networks. A salient concept underlying a great majority of these developments
has been the notion that a network displays a set of trees and those trees can
be used to infer, analyze, and study the network.
  In this paper, we show that in the presence of coalescence effects, the set
of displayed trees is not sufficient to capture the network. We formally define
the set of parental trees of a network and make three contributions based on
this definition. First, we extend the notion of anomaly zone to phylogenetic
networks and report on anomaly results for different networks. Second, we
demonstrate how coalescence events could negatively affect the ability to infer
a species tree that could be augmented into the correct network. Third, we
demonstrate how a phylogenetic network can be viewed as a mixture model that
lends itself to a novel inference approach via gene tree clustering.
  Our results demonstrate the limitations of focusing on the set of trees
displayed by a network when analyzing and inferring the network. Our findings
can form the basis for achieving higher accuracy when inferring phylogenetic
networks and open up new venues for research in this area, including new
problem formulations based on the notion of a network's parental trees.
A Relevant Content Filtering Based Framework For Data Stream Summarization
Authors: Dong Cailing, Agarwal Arvind
Date: 2016-06-23 00:00:00

Social media platforms are a rich source of information these days, however,
of all the available information, only a small fraction is of users' interest.
To help users catch up with the latest topics of their interests from the large
amount of information available in social media, we present a relevant content
filtering based framework for data stream summarization. More specifically,
given the topic or event of interest, this framework can dynamically discover
and filter out relevant information from irrelevant information in the stream
of text provided by social media platforms. It then further captures the most
representative and up-to-date information to generate a sequential summary or
event story line along with the evolution of the topic or event. Our framework
does not depend on any labeled data, it instead uses the weak supervision
provided by the user, which matches the real scenarios of users searching for
information about an ongoing event. We experimented on two real events traced
by a Twitter dataset from TREC 2011. The results verified the effectiveness of
relevant content filtering and sequential summary generation of the proposed
framework. It also shows its robustness of using the most easy-to-obtain weak
supervision, i.e., trending topic or hashtag. Thus, this framework can be
easily integrated into social media platforms such as Twitter to generate
sequential summaries for the events of interest. We also make the manually
generated gold-standard sequential summaries of the two test events publicly
available for future use in the community.
Spread, volatility, and volume relationship in financial markets and market making profit optimization
Date: 2016-06-23 00:00:00

We study the relationship between price spread, volatility and trading
volume. We find that spread forms as a result of interplay between order
liquidity and order impact. When trading volume is small adding more liquidity
helps improve price accuracy and reduce spread, but after some point additional
liquidity begins to deteriorate price. The model allows to connect the bid-ask
spread and high-low bars to measurable microstructural parameters and express
their dependence on trading volume, volatility and time horizon. Using the
established relations, we address the operating spread optimization problem to
maximize market-making profit.
Qualitative changes in spike-based neural coding and synchronization at the saddle-node loop bifurcation
Date: 2016-11-12 00:00:00

Information processing in the brain crucially depends on encoding properties
of single neurons, with particular relevance of the spike-generation mechanism.
The latter hinges upon the bifurcation type at the transition point between
resting state and limit cycle spiking. Prominent qualitative changes in
encoding have previously been attributed to a specific switch of such a
bifurcation at the Bogdanov-Takens (BT) point. This study unveils another,
highly relevant and so far underestimated transition point: the saddle-node
loop bifurcation. As we show, this bifurcation turns out to induce even more
drastic changes in spike-based coding than the BT transition. This result
arises from a direct effect of the saddle-node loop bifurcation on the limit
cycle and hence spike dynamics, in contrast to the BT bifurcation, whose
immediate influence is exerted upon the subthreshold dynamics and hence only
indirectly relates to spiking. We specifically demonstrate that the saddle-node
loop bifurcation (i) ubiquitously occurs in planar neuron models with a
saddle-node on invariant cycle onset bifurcation, and (ii) results in a
symmetry breaking of the system's phase-response curve. The latter entails
close to optimal coding and synchronization properties in event-based
information processing units, such as neurons. The saddle-node loop bifurcation
leads to a peak in synchronization range and provides an attractive mechanism
for the so far unresolved facilitation of high frequencies in neuronal
processing. The derived bifurcation structure is of interest in any system for
which a relaxation limit is admissible, such as Josephson junctions and
chemical oscillators. On the experimental side, our theory applies to optical
stimulation of nerve cells, and reveals that these techniques could manipulate
a variety of information processing characteristics in nerve cells beyond pure
activation.
Testing Modeling Assumptions in the West Africa Ebola Outbreak
Date: 2016-10-12 00:00:00

The Ebola virus in West Africa has infected almost 30,000 and killed over
11,000 people. Recent models of Ebola Virus Disease (EVD) have often made
assumptions about how the disease spreads, such as uniform transmissibility and
homogeneous mixing within a population. In this paper, we test whether these
assumptions are necessarily correct, and offer simple solutions that may
improve disease model accuracy. First, we use data and models of West African
migration to show that EVD does not homogeneously mix, but spreads in a
predictable manner. Next, we estimate the initial growth rate of EVD within
country administrative divisions and find that it significantly decreases with
population density. Finally, we test whether EVD strains have uniform
transmissibility through a novel statistical test, and find that certain
strains appear more often than expected by chance.
Actin filaments growing against a barrier with fluctuating shape
Date: 2016-06-24 00:00:00
Tasks: Local Distortion

We study force generation by a set of parallel actin filaments growing
against a non-rigid obstacle, in presence of an external load. The filaments
polymerize by either moving the whole obstacle, with a large energy cost, or by
causing local distortion in its shape which costs much less energy. The
non-rigid obstacle also has local thermal fluctuations due to which its shape
can change with time and we describe this using fluctuations in the height
profile of a one dimensional interface with Kardar-Parisi-Zhang dynamics. We
find the shape fluctuations of the barrier strongly affects the force
generation mechanism. The qualitative nature of the force-velocity curve is
crucially determined by the relative time-scale of filament and barrier
dynamics. The height profile of the barrier also shows interesting variation
with the external load. Our analytical calculation within mean-field theory
shows reasonable agreement with our simulation results.
Kategorisasi dokumen web secara otomatis berdasarkan folksonomy menggunakan multinomial naive Bayes classifier
Authors: Irawan Hendy
Date: 2016-06-24 00:00:00
Tasks: TAG

Folksonomy is a non-hierarchical document categorizing system, that treats
every category in a flat manner, dan every category is entered freely by anyone
who submitted a document in these categories. Categorization is done
automatically at the time a document is submitted, by entering the list of
categories that best fit the document. del.icio.us (http://del.icio.us) site is
one of the most popular social bookmarking sites that uses folksonomy.
  Usage of folksonomy, although very easy, also has its weaknesses, such as use
of different tags for the same concept, use of the same tag for different
concepts, no quality control, etc. We try to provide a solution for some of
these problems by analyzing Web documents' contents and categorizing them
automatically using multinomial naive Bayes algorithm.
  Bayes classifier works by using a set of evidences and a set of classes. By
training the system using sample data, we can determine the probability of an
evidence given a particular class. Bayes classifier also uses prior probability
of a class, which can be calculated from sample data. From these analysis, when
given a new document which is formed by a set of evidences (words), the
probabilities of each class given that document (posterior probabilities) can
be determined.
  This system is implemented using PHP 5, Apache, and MySQL. The conclusion
from building this system is that the Bayes method can be used to automatically
categorize documents and also as an assistive tool for manual categorization.
  -----
  Folksonomy merupakan metode kategorisasi dokumen yang tidak hierarkis,
menyamaratakan kedudukan setiap kategori, dan judul kategori ditentukan secara
bebas oleh siapa saja yang memasukkan sebuah dokumen di dalam kategori-kategori
tersebut.
Using Word Embeddings for Automatic Query Expansion
Authors: Roy Dwaipayan, Paul Debjyoti, Mitra Mandar, Garain Utpal
Date: 2016-06-24 00:00:00
Tasks: Language Modeling, Language Modelling, Retrieval, Word Embeddings

In this paper a framework for Automatic Query Expansion (AQE) is proposed
using distributed neural language model word2vec. Using semantic and contextual
relation in a distributed and unsupervised framework, word2vec learns a low
dimensional embedding for each vocabulary entry. Using such a framework, we
devise a query expansion technique, where related terms to a query are obtained
by K-nearest neighbor approach. We explore the performance of the AQE methods,
with and without feedback query expansion, and a variant of simple K-nearest
neighbor in the proposed framework. Experiments on standard TREC ad-hoc data
(Disk 4, 5 with query sets 301-450, 601-700) and web data (WT10G data with
query set 451-550) shows significant improvement over standard term-overlapping
based retrieval methods. However the proposed method fails to achieve
comparable performance with statistical co-occurrence based feedback method
such as RM3. We have also found that the word2vec based query expansion methods
perform similarly with and without any feedback information.