pwc-archive/papers-with-abstracts huggingface dataset:
A short note on estimating intelligence from user profiles in the context of universal psychometrics: prospects and caveats
Authors: Jose Hernandez-Orallo
Date: 2013-05-07 00:00:00
Tasks: Sentiment Analysis

There has been an increasing interest in inferring some personality traits
from users and players in social networks and games, respectively. This goes
beyond classical sentiment analysis, and also much further than customer
profiling. The purpose here is to have a characterisation of users in terms of
personality traits, such as openness, conscientiousness, extraversion,
agreeableness, and neuroticism. While this is an incipient area of research, we
ask the question of whether cognitive abilities, and intelligence in
particular, are also measurable from user profiles. However, we pose the
question as broadly as possible in terms of subjects, in the context of
universal psychometrics, including humans, machines and hybrids. Namely, in
this paper we analyse the following question: is it possible to measure the
intelligence of humans and (non-human) bots in a social network or a game just
from their user profiles, i.e., by observation, without the use of interactive
tests, such as IQ tests, the Turing test or other more principled machine
intelligence tests?
A Method for Visuo-Spatial Classification of Freehand Shapes Freely Sketched
Authors: Ney Renau-Ferrer, Céline Remi
Date: 2013-05-07 00:00:00
Tasks: Clustering, General Classification

We present the principle and the main steps of a new method for the
visuo-spatial analysis of geometrical sketches recorded online. Visuo-spatial
analysis is a necessary step for multi-level analysis. Multi-level analysis
simultaneously allows classification, comparison or clustering of the
constituent parts of a pattern according to their visuo-spatial properties,
their procedural strategies, their structural or temporal parameters, or any
combination of two or more of those parameters. The first results provided by
this method concern the comparison of sketches to some perfect patterns of
simple geometrical figures and the measure of dissimilarity between real
sketches. The mean rates of good decision higher than 95% obtained are
promising in both cases.
EURETILE 2010-2012 summary: first three years of activity of the European Reference Tiled Experiment
Authors: Pier Stanislao Paolucci, Iuliana Bacivarov, Gert Goossens, Rainer Leupers, Frédéric Rousseau, Christoph Schumacher, Lothar Thiele, Piero Vicini
Date: 2013-05-07 00:00:00

This is the summary of first three years of activity of the EURETILE FP7
project 247846. EURETILE investigates and implements brain-inspired and
fault-tolerant foundational innovations to the system architecture of massively
parallel tiled computer architectures and the corresponding programming
paradigm. The execution targets are a many-tile HW platform, and a many-tile
simulator. A set of SW process - HW tile mapping candidates is generated by the
holistic SW tool-chain using a combination of analytic and bio-inspired
methods. The Hardware dependent Software is then generated, providing OS
services with maximum efficiency/minimal overhead. The many-tile simulator
collects profiling data, closing the loop of the SW tool chain. Fine-grain
parallelism inside processes is exploited by optimized intra-tile compilation
techniques, but the project focus is above the level of the elementary tile.
The elementary HW tile is a multi-processor, which includes a fault tolerant
Distributed Network Processor (for inter-tile communication) and ASIP
accelerators. Furthermore, EURETILE investigates and implements the innovations
for equipping the elementary HW tile with high-bandwidth, low-latency
brain-like inter-tile communication emulating 3 levels of connection hierarchy,
namely neural columns, cortical areas and cortex, and develops a dedicated
cortical simulation benchmark: DPSNN-STDP (Distributed Polychronous Spiking
Neural Net with synaptic Spiking Time Dependent Plasticity). EURETILE leverages
on the multi-tile HW paradigm and SW tool-chain developed by the FET-ACA SHAPES
Integrated Project (2006-2009).
Speech Enhancement Modeling Towards Robust Speech Recognition System
Authors: Urmila Shrawankar, V. M. Thakare
Date: 2013-05-07 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Robust Speech Recognition, Sentence, Speech Enhancement, speech-recognition, Speech Recognition

Form about four decades human beings have been dreaming of an intelligent
machine which can master the natural speech. In its simplest form, this machine
should consist of two subsystems, namely automatic speech recognition (ASR) and
speech understanding (SU). The goal of ASR is to transcribe natural speech
while SU is to understand the meaning of the transcription. Recognizing and
understanding a spoken sentence is obviously a knowledge-intensive process,
which must take into account all variable information about the speech
communication process, from acoustics to semantics and pragmatics. While
developing an Automatic Speech Recognition System, it is observed that some
adverse conditions degrade the performance of the Speech Recognition System. In
this contribution, speech enhancement system is introduced for enhancing speech
signals corrupted by additive noise and improving the performance of Automatic
Speech Recognizers in noisy conditions. Automatic speech recognition
experiments show that replacing noisy speech signals by the corresponding
enhanced speech signals leads to an improvement in the recognition accuracies.
The amount of improvement varies with the type of the corrupting noise.
A Differential Equations Approach to Optimizing Regret Trade-offs
Authors: Alexandr Andoni, Rina Panigrahy
Date: 2013-05-07 00:00:00

We consider the classical question of predicting binary sequences and study
the {\em optimal} algorithms for obtaining the best possible regret and payoff
functions for this problem. The question turns out to be also equivalent to the
problem of optimal trade-offs between the regrets of two experts in an "experts
problem", studied before by \cite{kearns-regret}. While, say, a regret of
$\Theta(\sqrt{T})$ is known, we argue that it important to ask what is the
provably optimal algorithm for this problem --- both because it leads to
natural algorithms, as well as because regret is in fact often comparable in
magnitude to the final payoffs and hence is a non-negligible term.
  In the basic setting, the result essentially follows from a classical result
of Cover from '65. Here instead, we focus on another standard setting, of
time-discounted payoffs, where the final "stopping time" is not specified. We
exhibit an explicit characterization of the optimal regret for this setting.
  To obtain our main result, we show that the optimal payoff functions have to
satisfy the Hermite differential equation, and hence are given by the solutions
to this equation. It turns out that characterization of the payoff function is
qualitatively different from the classical (non-discounted) setting, and,
namely, there's essentially a unique optimal solution.
Speckle Noise Reduction in Medical Ultrasound Images
Authors: Faouzi Benzarti, Hamid Amiri
Date: 2013-05-06 00:00:00
Tasks: Denoising, Diagnostic

Ultrasound imaging is an incontestable vital tool for diagnosis, it provides
in non-invasive manner the internal structure of the body to detect eventually
diseases or abnormalities tissues. Unfortunately, the presence of speckle noise
in these images affects edges and fine details which limit the contrast
resolution and make diagnostic more difficult. In this paper, we propose a
denoising approach which combines logarithmic transformation and a non linear
diffusion tensor. Since speckle noise is multiplicative and nonwhite process,
the logarithmic transformation is a reasonable choice to convert
signaldependent or pure multiplicative noise to an additive one. The key idea
from using diffusion tensor is to adapt the flow diffusion towards the local
orientation by applying anisotropic diffusion along the coherent structure
direction of interesting features in the image. To illustrate the effective
performance of our algorithm, we present some experimental results on
synthetically and real echographic images.
Towards an Author-Topic-Term-Model Visualization of 100 Years of German Sociological Society Proceedings
Authors: Arnim Bleier, Andreas Strotmann
Date: 2013-05-06 00:00:00
Tasks: Bayesian Inference

Author co-citation studies employ factor analysis to reduce high-dimensional
co-citation matrices to low-dimensional and possibly interpretable factors, but
these studies do not use any information from the text bodies of publications.
We hypothesise that term frequencies may yield useful information for
scientometric analysis. In our work we ask if word features in combination with
Bayesian analysis allow well-founded science mapping studies. This work goes
back to the roots of Mosteller and Wallace's (1964) statistical text analysis
using word frequency features and a Bayesian inference approach, tough with
different goals. To answer our research question we (i) introduce a new data
set on which the experiments are carried out, (ii) describe the Bayesian model
employed for inference and (iii) present first results of the analysis.
Extending Modern SAT Solvers for Enumerating All Models
Authors: Said Jabbour, Lakhdar Sais, Yakoub Salhi
Date: 2013-05-02 00:00:00
Tasks: All

In this paper, we address the problem of enumerating all models of a Boolean
formula in conjunctive normal form (CNF). We propose an extension of CDCL-based
SAT solvers to deal with this fundamental problem. Then, we provide an
experimental evaluation of our proposed SAT model enumeration algorithms on
both satisfiable SAT instances taken from the last SAT challenge and on
instances from the SAT-based encoding of sequence mining problems.
New Alignment Methods for Discriminative Book Summarization
Authors: David Bamman, Noah A. Smith
Date: 2013-05-06 00:00:00
Tasks: Book summarization

We consider the unsupervised alignment of the full text of a book with a
human-written summary. This presents challenges not seen in other text
alignment problems, including a disparity in length and, consequent to this, a
violation of the expectation that individual words and phrases should align,
since large passages and chapters can be distilled into a single summary
phrase. We present two new methods, based on hidden Markov models, specifically
targeted to this problem, and demonstrate gains on an extractive book
summarization task. While there is still much room for improvement,
unsupervised alignment holds intrinsic value in offering insight into what
features of a book are deemed worthy of summarization.
A Convex Functional for Image Denoising based on Patches with Constrained Overlaps and its vectorial application to Low Dose Differential Phase Tomography
Authors: Alessandro Mirone, Emmanuel Brun, Paola Coan
Date: 2013-05-06 00:00:00
Tasks: Denoising, Dictionary Learning, Image Denoising

We solve the image denoising problem with a dictionary learning technique by
writing a convex functional of a new form. This functional contains beside the
usual sparsity inducing term and fidelity term, a new term which induces
similarity between overlapping patches in the overlap regions. The functional
depends on two free regularization parameters: a coefficient multiplying the
sparsity-inducing $L_{1}$ norm of the patch basis functions coefficients, and a
coefficient multiplying the $L_{2}$ norm of the differences between patches in
the overlapping regions. The solution is found by applying the iterative
proximal gradient descent method with FISTA acceleration. In the case of
tomography reconstruction we calculate the gradient by applying projection of
the solution and its error backprojection at each iterative step. We study the
quality of the solution, as a function of the regularization parameters and
noise, on synthetic datas for which the solution is a-priori known. We apply
the method on experimental data in the case of Differential Phase Tomography.
For this case we use an original approach which consists in using vectorial
patches, each patch having two components: one per each gradient component. The
resulting algorithm, implemented in the ESRF tomography reconstruction code
PyHST, results to be robust, efficient, and well adapted to strongly reduce the
required dose and the number of projections in medical tomography.
A Contrario Selection of Optimal Partitions for Image Segmentation
Authors: Juan Cardelino, Vicent Caselles, Marcelo Bertalmio, Gregory Randall
Date: 2013-05-06 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

We present a novel segmentation algorithm based on a hierarchical
representation of images. The main contribution of this work is to explore the
capabilities of the A Contrario reasoning when applied to the segmentation
problem, and to overcome the limitations of current algorithms within that
framework. This exploratory approach has three main goals.
  Our first goal is to extend the search space of greedy merging algorithms to
the set of all partitions spanned by a certain hierarchy, and to cast the
segmentation as a selection problem within this space. In this way we increase
the number of tested partitions and thus we potentially improve the
segmentation results. In addition, this space is considerably smaller than the
space of all possible partitions, thus we still keep the complexity controlled.
  Our second goal aims to improve the locality of region merging algorithms,
which usually merge pairs of neighboring regions. In this work, we overcome
this limitation by introducing a validation procedure for complete partitions,
rather than for pairs of regions.
  The third goal is to perform an exhaustive experimental evaluation
methodology in order to provide reproducible results.
  Finally, we embed the selection process on a statistical A Contrario
framework which allows us to have only one free parameter related to the
desired scale.
Gromov-Hausdorff Approximation of Metric Spaces with Linear Structure
Authors: Frédéric Chazal, Jian Sun
Date: 2013-05-06 00:00:00

In many real-world applications data come as discrete metric spaces sampled
around 1-dimensional filamentary structures that can be seen as metric graphs.
In this paper we address the metric reconstruction problem of such filamentary
structures from data sampled around them. We prove that they can be
approximated, with respect to the Gromov-Hausdorff distance by well-chosen Reeb
graphs (and some of their variants) and we provide an efficient and easy to
implement algorithm to compute such approximations in almost linear time. We
illustrate the performances of our algorithm on a few synthetic and real data
sets.
Multi-Objective AI Planning: Comparing Aggregation and Pareto Approaches
Authors: Mostepha Redouane Khouadjia, Marc Schoenauer, Vincent Vidal, Johann Dréo, Pierre Savéant
Date: 2013-05-06 00:00:00
Tasks: Evolutionary Algorithms

Most real-world Planning problems are multi-objective, trying to minimize
both the makespan of the solution plan, and some cost of the actions involved
in the plan. But most, if not all existing approaches are based on
single-objective planners, and use an aggregation of the objectives to remain
in the single-objective context. Divide and Evolve (DaE) is an evolutionary
planner that won the temporal deterministic satisficing track at the last
International Planning Competitions (IPC). Like all Evolutionary Algorithms
(EA), it can easily be turned into a Pareto-based Multi-Objective EA. It is
however important to validate the resulting algorithm by comparing it with the
aggregation approach: this is the goal of this paper. The comparative
experiments on a recently proposed benchmark set that are reported here
demonstrate the usefulness of going Pareto-based in AI Planning.
A Computer Vision System for Attention Mapping in SLAM based 3D Models
Authors: Lucas Paletta, Katrin Santner, Gerald Fritz, Albert Hofmann, Gerald Lodron, Georg Thallinger, Heinz Mayer
Date: 2013-05-06 00:00:00

The study of human factors in the frame of interaction studies has been
relevant for usability engi-neering and ergonomics for decades. Today, with the
advent of wearable eye-tracking and Google glasses, monitoring of human factors
will soon become ubiquitous. This work describes a computer vision system that
enables pervasive mapping and monitoring of human attention. The key
contribu-tion is that our methodology enables full 3D recovery of the gaze
pointer, human view frustum and associated human centred measurements directly
into an automatically computed 3D model in real-time. We apply RGB-D SLAM and
descriptor matching methodologies for the 3D modelling, locali-zation and fully
automated annotation of ROIs (regions of interest) within the acquired 3D
model. This innovative methodology will open new avenues for attention studies
in real world environments, bringing new potential into automated processing
for human factors technologies.
Techniques for Feature Extraction In Speech Recognition System : A Comparative Study
Authors: Urmila Shrawankar, V. M. Thakare
Date: 2013-05-06 00:00:00
Tasks: speech-recognition, Speech Recognition

The time domain waveform of a speech signal carries all of the auditory
information. From the phonological point of view, it little can be said on the
basis of the waveform itself. However, past research in mathematics, acoustics,
and speech technology have provided many methods for converting data that can
be considered as information if interpreted correctly. In order to find some
statistically relevant information from incoming data, it is important to have
mechanisms for reducing the information of each segment in the audio signal
into a relatively small number of parameters, or features. These features
should describe each segment in such a characteristic way that other similar
segments can be grouped together by comparing their features. There are
enormous interesting and exceptional ways to describe the speech signal in
terms of parameters. Though, they all have their strengths and weaknesses, we
have presented some of the most used methods with their importance.
Learning Human Activities and Object Affordances from RGB-D Videos
Authors: Hema Swetha Koppula, Rudhir Gupta, Ashutosh Saxena
Date: 2012-10-04 00:00:00
Tasks: Descriptive, Object, Skeleton Based Action Recognition

Understanding human activities and object affordances are two very important
skills, especially for personal robots which operate in human environments. In
this work, we consider the problem of extracting a descriptive labeling of the
sequence of sub-activities being performed by a human, and more importantly, of
their interactions with the objects in the form of associated affordances.
Given a RGB-D video, we jointly model the human activities and object
affordances as a Markov random field where the nodes represent objects and
sub-activities, and the edges represent the relationships between object
affordances, their relations with sub-activities, and their evolution over
time. We formulate the learning problem using a structural support vector
machine (SSVM) approach, where labelings over various alternate temporal
segmentations are considered as latent variables. We tested our method on a
challenging dataset comprising 120 activity videos collected from 4 subjects,
and obtained an accuracy of 79.4% for affordance, 63.4% for sub-activity and
75.0% for high-level activity labeling. We then demonstrate the use of such
descriptive labeling in performing assistive tasks by a PR2 robot.
Diffusion Adaptation over Networks
Authors: Ali H. Sayed
Date: 2012-05-18 00:00:00
Tasks: Distributed Optimization

Adaptive networks are well-suited to perform decentralized information
processing and optimization tasks and to model various types of self-organized
and complex behavior encountered in nature. Adaptive networks consist of a
collection of agents with processing and learning abilities. The agents are
linked together through a connection topology, and they cooperate with each
other through local interactions to solve distributed optimization, estimation,
and inference problems in real-time. The continuous diffusion of information
across the network enables agents to adapt their performance in relation to
streaming data and network conditions; it also results in improved adaptation
and learning performance relative to non-cooperative agents. This article
provides an overview of diffusion strategies for adaptation and learning over
networks. The article is divided into several sections: 1. Motivation; 2.
Mean-Square-Error Estimation; 3. Distributed Optimization via Diffusion
Strategies; 4. Adaptive Diffusion Strategies; 5. Performance of
Steepest-Descent Diffusion Strategies; 6. Performance of Adaptive Diffusion
Strategies; 7. Comparing the Performance of Cooperative Strategies; 8.
Selecting the Combination Weights; 9. Diffusion with Noisy Information
Exchanges; 10. Extensions and Further Considerations; Appendix A: Properties of
Kronecker Products; Appendix B: Graph Laplacian and Network Connectivity;
Appendix C: Stochastic Matrices; Appendix D: Block Maximum Norm; Appendix E:
Comparison with Consensus Strategies; References.
On Rational Closure in Description Logics of Typicality
Authors: Laura Giordano, Valentina Gliozzi, Nicola Olivetti, Gian Luca Pozzato
Date: 2013-05-05 00:00:00

We define the notion of rational closure in the context of Description Logics
extended with a tipicality operator. We start from ALC+T, an extension of ALC
with a typicality operator T: intuitively allowing to express concepts of the
form T(C), meant to select the "most normal" instances of a concept C. The
semantics we consider is based on rational model. But we further restrict the
semantics to minimal models, that is to say, to models that minimise the rank
of domain elements. We show that this semantics captures exactly a notion of
rational closure which is a natural extension to Description Logics of Lehmann
and Magidor's original one. We also extend the notion of rational closure to
the Abox component. We provide an ExpTime algorithm for computing the rational
closure of an Abox and we show that it is sound and complete with respect to
the minimal model semantics.
Hybridization of Otsu Method and Median Filter for Color Image Segmentation
Authors: Firas Ajil Jassim, Fawzi H. Altaani
Date: 2013-05-05 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

In this article a novel algorithm for color image segmentation has been
developed. The proposed algorithm based on combining two existing methods in
such a novel way to obtain a significant method to partition the color image
into significant regions. On the first phase, the traditional Otsu method for
gray channel image segmentation were applied for each of the R,G, and B
channels separately to determine the suitable automatic threshold for each
channel. After that, the new modified channels are integrated again to
formulate a new color image. The resulted image suffers from some kind of
distortion. To get rid of this distortion, the second phase is arise which is
the median filter to smooth the image and increase the segmented regions. This
process looks very significant by the ocular eye. Experimental results were
presented on a variety of test images to support the proposed algorithm.