pwc-archive/papers-with-abstracts huggingface dataset:
Spectral classification using convolutional neural networks
Authors: Pavel Hála
Date: 2014-12-29 00:00:00
Tasks: Classification, General Classification

There is a great need for accurate and autonomous spectral classification
methods in astrophysics. This thesis is about training a convolutional neural
network (ConvNet) to recognize an object class (quasar, star or galaxy) from
one-dimension spectra only. Author developed several scripts and C programs for
datasets preparation, preprocessing and postprocessing of the data. EBLearn
library (developed by Pierre Sermanet and Yann LeCun) was used to create
ConvNets. Application on dataset of more than 60000 spectra yielded success
rate of nearly 95%. This thesis conclusively proved great potential of
convolutional neural networks and deep learning methods in astrophysics.
Globally Optimal Joint Image Segmentation and Shape Matching Based on Wasserstein Modes
Authors: Bernhard Schmitzer, Christoph Schnörr
Date: 2014-07-15 00:00:00
Tasks: Image Segmentation, Semantic Segmentation

A functional for joint variational object segmentation and shape matching is
developed. The formulation is based on optimal transport w.r.t. geometric
distance and local feature similarity. Geometric invariance and modelling of
object-typical statistical variations is achieved by introducing degrees of
freedom that describe transformations and deformations of the shape template.
The shape model is mathematically equivalent to contour-based approaches but
inference can be performed without conversion between the contour and region
representations, allowing combination with other convex segmentation approaches
and simplifying optimization. While the overall functional is non-convex,
non-convexity is confined to a low-dimensional variable. We propose a locally
optimal alternating optimization scheme and a globally optimal branch and bound
scheme, based on adaptive convex relaxation. Combining both methods allows to
eliminate the delicate initialization problem inherent to many contour based
approaches while remaining computationally practical. The properties of the
functional, its ability to adapt to a wide range of input data structures and
the different optimization schemes are illustrated and compared by numerical
experiments.
Improving approximate RPCA with a k-sparsity prior
Authors: Maximilian Karl, Christian Osendorfer
Date: 2014-12-29 00:00:00
Tasks: General Classification

A process centric view of robust PCA (RPCA) allows its fast approximate
implementation based on a special form o a deep neural network with weights
shared across all layers. However, empirically this fast approximation to RPCA
fails to find representations that are parsemonious. We resolve these bad local
minima by relaxing the elementwise L1 and L2 priors and instead utilize a
structure inducing k-sparsity prior. In a discriminative classification task
the newly learned representations outperform these from the original
approximate RPCA formulation significantly.
Rigid and Non-rigid Shape Evolutions for Shape Alignment and Recovery in Images
Authors: Junyan Wang, Kap-Luk Chan
Date: 2014-12-29 00:00:00

The same type of objects in different images may vary in their shapes because
of rigid and non-rigid shape deformations, occluding foreground as well as
cluttered background. The problem concerned in this work is the shape
extraction in such challenging situations. We approach the shape extraction
through shape alignment and recovery. This paper presents a novel and general
method for shape alignment and recovery by using one example shapes based on
deterministic energy minimization. Our idea is to use general model of shape
deformation in minimizing active contour energies. Given \emph{a priori} form
of the shape deformation, we show how the curve evolution equation
corresponding to the shape deformation can be derived. The curve evolution is
called the prior variation shape evolution (PVSE). We also derive the
energy-minimizing PVSE for minimizing active contour energies. For shape
recovery, we propose to use the PVSE that deforms the shape while preserving
its shape characteristics. For choosing such shape-preserving PVSE, a theory of
shape preservability of the PVSE is established. Experimental results validate
the theory and the formulations, and they demonstrate the effectiveness of our
method.
Improving Persian Document Classification Using Semantic Relations between Words
Authors: Saeed Parseh, Ahmad Baraani
Date: 2014-12-28 00:00:00
Tasks: Classification, Document Classification, General Classification, Management

With the increase of information, document classification as one of the
methods of text mining, plays vital role in many management and organizing
information. Document classification is the process of assigning a document to
one or more predefined category labels. Document classification includes
different parts such as text processing, term selection, term weighting and
final classification. The accuracy of document classification is very
important. Thus improvement in each part of classification should lead to
better results and higher precision. Term weighting has a great impact on the
accuracy of the classification. Most of the existing weighting methods exploit
the statistical information of terms in documents and do not consider semantic
relations between words. In this paper, an automated document classification
system is presented that uses a novel term weighting method based on semantic
relations between terms. To evaluate the proposed method, three standard
Persian corpuses are used. Experiment results show 2 to 4 percent improvement
in classification accuracy compared with the best previous designed system for
Persian documents.
A DDoS-Aware IDS Model Based on Danger Theory and Mobile Agents
Authors: Mahdi Zamani, Mahnush Movahedi, Mohammad Ebadzadeh, Hossein Pedram
Date: 2013-12-31 00:00:00
Tasks: Intrusion Detection

We propose an artificial immune model for intrusion detection in distributed
systems based on a relatively recent theory in immunology called Danger theory.
Based on Danger theory, immune response in natural systems is a result of
sensing corruption as well as sensing unknown substances. In contrast,
traditional self-nonself discrimination theory states that immune response is
only initiated by sensing nonself (unknown) patterns. Danger theory solves many
problems that could only be partially explained by the traditional model.
Although the traditional model is simpler, such problems result in high false
positive rates in immune-inspired intrusion detection systems. We believe using
danger theory in a multi-agent environment that computationally emulates the
behavior of natural immune systems is effective in reducing false positive
rates. We first describe a simplified scenario of immune response in natural
systems based on danger theory and then, convert it to a computational model as
a network protocol. In our protocol, we define several immune signals and model
cell signaling via message passing between agents that emulate cells. Most
messages include application-specific patterns that must be meaningfully
extracted from various system properties. We show how to model these messages
in practice by performing a case study on the problem of detecting distributed
denial-of-service attacks in wireless sensor networks. We conduct a set of
systematic experiments to find a set of performance metrics that can accurately
distinguish malicious patterns. The results indicate that the system can be
efficiently used to detect malicious patterns with a high level of accuracy.
Proceedings of the 11th workshop on Quantum Physics and Logic
Authors: Bob Coecke, Ichiro Hasuo, Prakash Panangaden
Date: 2014-12-28 00:00:00

This volume contains the proceedings of the 11th International Workshop on
Quantum Physics and Logic (QPL 2014), which was held from the 4th to the 6th of
June, 2014, at Kyoto University, Japan.
  The goal of the QPL workshop series is to bring together researchers working
on mathematical foundations of quantum physics, quantum computing and
spatio-temporal causal structures, and in particular those that use logical
tools, ordered algebraic and category-theoretic structures, formal languages,
semantic methods and other computer science methods for the study of physical
behavior in general. Over the past few years, there has been growing activity
in these foundational approaches, together with a renewed interest in the
foundations of quantum theory, which complement the more mainstream research in
quantum computation. Earlier workshops in this series, with the same acronym
under the name "Quantum Programming Languages", were held in Ottawa (2003),
Turku (2004), Chicago (2005), and Oxford (2006). The first QPL under the new
name Quantum Physics and Logic was held in Reykjavik (2008), followed by Oxford
(2009 and 2010), Nijmegen (2011), Brussels (2012) and Barcelona (2013).
Persian Sentiment Analyzer: A Framework based on a Novel Feature Selection Method
Authors: Ayoub Bagheri, Mohamad Saraee
Date: 2014-12-27 00:00:00
Tasks: Classification, feature selection, General Classification, Information Retrieval, Lemmatization, Persian Sentiment Analysis, Retrieval, Sentiment Analysis, Sentiment Classification

In the recent decade, with the enormous growth of digital content in internet
and databases, sentiment analysis has received more and more attention between
information retrieval and natural language processing researchers. Sentiment
analysis aims to use automated tools to detect subjective information from
reviews. One of the main challenges in sentiment analysis is feature selection.
Feature selection is widely used as the first stage of analysis and
classification tasks to reduce the dimension of problem, and improve speed by
the elimination of irrelevant and redundant features. Up to now as there are
few researches conducted on feature selection in sentiment analysis, there are
very rare works for Persian sentiment analysis. This paper considers the
problem of sentiment classification using different feature selection methods
for online customer reviews in Persian language. Three of the challenges of
Persian text are using of a wide variety of declensional suffixes, different
word spacing and many informal or colloquial words. In this paper we study
these challenges by proposing a model for sentiment classification of Persian
review documents. The proposed model is based on lemmatization and feature
selection and is employed Naive Bayes algorithm for classification. We evaluate
the performance of the model on a manually gathered collection of cellphone
reviews, where the results show the effectiveness of the proposed approaches.
Functional correspondence by matrix completion
Authors: Artiom Kovnatsky, Michael M. Bronstein, Xavier Bresson, Pierre Vandergheynst
Date: 2014-12-27 00:00:00
Proceeding: CVPR 2015 6
Tasks: Matrix Completion

In this paper, we consider the problem of finding dense intrinsic
correspondence between manifolds using the recently introduced functional
framework. We pose the functional correspondence problem as matrix completion
with manifold geometric structure and inducing functional localization with the
$L_1$ norm. We discuss efficient numerical procedures for the solution of our
problem. Our method compares favorably to the accuracy of state-of-the-art
correspondence algorithms on non-rigid shape matching benchmarks, and is
especially advantageous in settings when only scarce data is available.
Scalable detection of statistically significant communities and hierarchies, using message-passing for modularity
Authors: Pan Zhang, Cristopher Moore
Date: 2014-03-23 00:00:00
Tasks: Stochastic Block Model

Modularity is a popular measure of community structure. However, maximizing
the modularity can lead to many competing partitions, with almost the same
modularity, that are poorly correlated with each other. It can also produce
illusory "communities" in random graphs where none exist. We address this
problem by using the modularity as a Hamiltonian at finite temperature, and
using an efficient Belief Propagation algorithm to obtain the consensus of many
partitions with high modularity, rather than looking for a single partition
that maximizes it. We show analytically and numerically that the proposed
algorithm works all the way down to the detectability transition in networks
generated by the stochastic block model. It also performs well on real-world
networks, revealing large communities in some networks where previous work has
claimed no communities exist. Finally we show that by applying our algorithm
recursively, subdividing communities until no statistically-significant
subcommunities can be found, we can detect hierarchical structure in real-world
networks more efficiently than previous methods.
Learning of Proto-object Representations via Fixations on Low Resolution
Authors: Chengyao Shen, Xun Huang, Qi Zhao
Date: 2014-12-23 00:00:00
Tasks: Object

While previous researches in eye fixation prediction typically rely on
integrating low-level features (e.g. color, edge) to form a saliency map,
recently it has been found that the structural organization of these features
into a proto-object representation can play a more significant role. In this
work, we present a computational framework based on deep network to demonstrate
that proto-object representations can be learned from low-resolution image
patches from fixation regions. We advocate the use of low-resolution inputs in
this work due to the following reasons: (1) Proto-objects are computed in
parallel over an entire visual field (2) People can perceive or recognize
objects well even it is in low resolution. (3) Fixations from lower resolution
images can predict fixations on higher resolution images. In the proposed
computational model, we extract multi-scale image patches on fixation regions
from eye fixation datasets, resize them to low resolution and feed them into a
hierarchical. With layer-wise unsupervised feature learning, we find that many
proto-objects like features responsive to different shapes of object blobs are
learned out. Visualizations also show that these features are selective to
potential objects in the scene and the responses of these features work well in
predicting eye fixations on the images when combined with learned weights.
Construction of Vietnamese SentiWordNet by using Vietnamese Dictionary
Authors: Xuan-Son Vu, Seong-Bae Park
Date: 2014-12-27 00:00:00
Tasks: Opinion Mining, Sentiment Analysis

SentiWordNet is an important lexical resource supporting sentiment analysis
in opinion mining applications. In this paper, we propose a novel approach to
construct a Vietnamese SentiWordNet (VSWN). SentiWordNet is typically generated
from WordNet in which each synset has numerical scores to indicate its opinion
polarities. Many previous studies obtained these scores by applying a machine
learning method to WordNet. However, Vietnamese WordNet is not available
unfortunately by the time of this paper. Therefore, we propose a method to
construct VSWN from a Vietnamese dictionary, not from WordNet. We show the
effectiveness of the proposed method by generating a VSWN with 39,561 synsets
automatically. The method is experimentally tested with 266 synsets with aspect
of positivity and negativity. It attains a competitive result compared with
English SentiWordNet that is 0.066 and 0.052 differences for positivity and
negativity sets respectively.
On the Relation between Realizable and Nonrealizable Cases of the Sequence Prediction Problem
Authors: Daniil Ryabko
Date: 2010-05-31 00:00:00
Tasks: Relation

A sequence $x_1,\dots,x_n,\dots$ of discrete-valued observations is generated
according to some unknown probabilistic law (measure) $\mu$. After observing
each outcome, one is required to give conditional probabilities of the next
observation. The realizable case is when the measure $\mu$ belongs to an
arbitrary but known class $\mathcal C$ of process measures. The non-realizable
case is when $\mu$ is completely arbitrary, but the prediction performance is
measured with respect to a given set $\mathcal C$ of process measures. We are
interested in the relations between these problems and between their solutions,
as well as in characterizing the cases when a solution exists and finding these
solutions. We show that if the quality of prediction is measured using the
total variation distance, then these problems coincide, while if it is measured
using the expected average KL divergence, then they are different. For some of
the formalizations we also show that when a solution exists, it can be obtained
as a Bayes mixture over a countable subset of $\mathcal C$. We also obtain
several characterization of those sets $\mathcal C$ for which solutions to the
considered problems exist. As an illustration to the general results obtained,
we show that a solution to the non-realizable case of the sequence prediction
problem exists for the set of all finite-memory processes, but does not exist
for the set of all stationary processes.
  It should be emphasized that the framework is completely general: the
processes measures considered are not required to be i.i.d., mixing,
stationary, or to belong to any parametric family.
Predicting User Engagement in Twitter with Collaborative Ranking
Authors: Ernesto Diaz-Aviles, Hoang Thanh Lam, Fabio Pinelli, Stefano Braghin, Yiannis Gkoufas, Michele Berlingerio, Francesco Calabrese
Date: 2014-12-26 00:00:00
Tasks: Collaborative Filtering, Collaborative Ranking, Recommendation Systems

Collaborative Filtering (CF) is a core component of popular web-based
services such as Amazon, YouTube, Netflix, and Twitter. Most applications use
CF to recommend a small set of items to the user. For instance, YouTube
presents to a user a list of top-n videos she would likely watch next based on
her rating and viewing history. Current methods of CF evaluation have been
focused on assessing the quality of a predicted rating or the ranking
performance for top-n recommended items. However, restricting the recommender
system evaluation to these two aspects is rather limiting and neglects other
dimensions that could better characterize a well-perceived recommendation. In
this paper, instead of optimizing rating or top-n recommendation, we focus on
the task of predicting which items generate the highest user engagement. In
particular, we use Twitter as our testbed and cast the problem as a
Collaborative Ranking task where the rich features extracted from the metadata
of the tweets help to complement the transaction information limited to user
ids, item ids, ratings and timestamps. We learn a scoring function that
directly optimizes the user engagement in terms of nDCG@10 on the predicted
ranking. Experiments conducted on an extended version of the MovieTweetings
dataset, released as part of the RecSys Challenge 2014, show the effectiveness
of our approach.
Context-Aware Analytics in MOM Applications
Authors: Martin Ringsquandl, Steffen Lamparter, Raffaello Lepratti
Date: 2014-12-26 00:00:00
Tasks: Data Integration, ERP, Management

Manufacturing Operations Management (MOM) systems are complex in the sense
that they integrate data from heterogeneous systems inside the automation
pyramid. The need for context-aware analytics arises from the dynamics of these
systems that influence data generation and hamper comparability of analytics,
especially predictive models (e.g. predictive maintenance), where concept drift
affects application of these models in the future. Recently, an increasing
amount of research has been directed towards data integration using semantic
context models. Manual construction of such context models is an elaborate and
error-prone task. Therefore, we pose the challenge to apply combinations of
knowledge extraction techniques in the domain of analytics in MOM, which
comprises the scope of data integration within Product Life-cycle Management
(PLM), Enterprise Resource Planning (ERP), and Manufacturing Execution Systems
(MES). We describe motivations, technological challenges and show benefits of
context-aware analytics, which leverage from and regard the interconnectedness
of semantic context data. Our example scenario shows the need for distribution
and effective change tracking of context information.
Different Types of Conflicting Knowledge in AmI Environments
Authors: Martin Homola, Theodore Patkos
Date: 2014-12-26 00:00:00

We characterize different types of conflicts that may occur in complex
distributed multi-agent scenarios, such as in Ambient Intelligence (AmI)
environments, and we argue that these conflicts should be resolved in a
suitable order and with the appropriate strategies for each individual conflict
type. We call for further research with the goal of turning conflict resolution
in AmI environments and similar multi-agent domains into a more coordinated and
agreed upon process.
Adding Context to Knowledge and Action Bases
Authors: Diego Calvanese, İsmail İlkan Ceylan, Marco Montali, Ario Santoso
Date: 2014-12-26 00:00:00

Knowledge and Action Bases (KABs) have been recently proposed as a formal
framework to capture the dynamics of systems which manipulate Description Logic
(DL) Knowledge Bases (KBs) through action execution. In this work, we enrich
the KAB setting with contextual information, making use of different context
dimensions. On the one hand, context is determined by the environment using
context-changing actions that make use of the current state of the KB and the
current context. On the other hand, it affects the set of TBox assertions that
are relevant at each time point, and that have to be considered when processing
queries posed over the KAB. Here we extend to our enriched setting the results
on verification of rich temporal properties expressed in mu-calculus, which had
been established for standard KABs. Specifically, we show that under a
run-boundedness condition, verification stays decidable.
Knowledge Propagation in Contextualized Knowledge Repositories: an Experimental Evaluation
Authors: Loris Bozzato, Luciano Serafini
Date: 2014-12-26 00:00:00
Tasks: Management

As the interest in the representation of context dependent knowledge in the
Semantic Web has been recognized, a number of logic based solutions have been
proposed in this regard. In our recent works, in response to this need, we
presented the description logic-based Contextualized Knowledge Repository (CKR)
framework. CKR is not only a theoretical framework, but it has been effectively
implemented over state-of-the-art tools for the management of Semantic Web
data: inference inside and across contexts has been realized in the form of
forward SPARQL-based rules over different RDF named graphs. In this paper we
present the first evaluation results for such CKR implementation. In
particular, in first experiment we study its scalability with respect to
different reasoning regimes. In a second experiment we analyze the effects of
knowledge propagation on the computation of inferences.
Texture analysis by multi-resolution fractal descriptors
Authors: João B. Florindo, Odemir M. Bruno
Date: 2014-12-26 00:00:00
Tasks: General Classification, Texture Classification

This work proposes a texture descriptor based on fractal theory. The method
is based on the Bouligand-Minkowski descriptors. We decompose the original
image recursively into 4 equal parts. In each recursion step, we estimate the
average and the deviation of the Bouligand-Minkowski descriptors computed over
each part. Thus, we extract entropy features from both average and deviation.
The proposed descriptors are provided by the concatenation of such measures.
The method is tested in a classification experiment under well known datasets,
that is, Brodatz and Vistex. The results demonstrate that the proposed
technique achieves better results than classical and state-of-the-art texture
descriptors, such as Gabor-wavelets and co-occurrence matrix.