pinecone/core-2020-05-10-deduplication huggingface dataset:
Evolutionary support vector machines for time series forecasting

Abstract. Time Series Forecasting (TSF) uses past patterns of an event in order to predict its future values and is a key tool to support decision making. In the last decades, Computational Intelligence (CI) techniques, such as Artificial Neural Networks (ANN) and more recently Support Vector Machines (SVM), have been proposed for TSF. The accuracy of the best CI model is affected by both the selection of input time lags and the model’s hyperparameters. In this work, we propose a novel Evolutionary SVM (ESVM) approach for TSF based on the Estimation Distribution Algorithm to search for the best number of inputs and SVM hyperparameters. Several experiments were held, using a set of six time series from distinct real-world domains. Overall, the proposed ESVM is competitive when compared with an Evolutionary ANN (EANN) and the popular ARIMA methodology, while consuming less computational effort when compared with EANN.The research reported here has been supported by FEDER (program COMPETE and FCT) under project FCOMP-01-0124-FEDER-02267
Diffusion maps and local models for wind power prediction

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-33266-1_70Proceedings of 22nd International Conference on Artificial Neural Networks, Lausanne, Switzerland, September 11-14, 2012In this work we will apply Diffusion Maps (DM), a recent technique for dimensionality reduction and clustering, to build local models for wind energy forecasting. We will compare ridge regression models for K–means clusters obtained over DM features, against the models obtained for clusters constructed over the original meteorological data or principal components, and also against a global model. We will see that a combination of the DM model for the low wind power region and the global model elsewhere outperforms other options.With partial support from grant TIN2010-21575-C02-01
of Spain’s Ministerio de Economía y Competitividad and the UAM–ADIC Chair for Machine Learning in Modelling and Prediction. The first author is also supported by an FPI-UAM grant and kindly thanks the Applied Mathematics Department
of Yale University for receiving her during a visit. The second author is
supported by the FPU-MEC grant AP2008-00167. We also thank Red Eléctrica de España, Spain’s TSO, for providing historic wind energy dat
Pair-associate Learning with Modulated Spike-Time Dependent Plasticity

We propose an associative learning model using reward mod- ulated spike-time dependent plasticity in reinforcement learning paradigm. The task of learning is to associate a stimulus pair, known as the predictor− choice pair, to a target response. In our model, a generic architecture of neural network has been used, with minimal assumption about the network dynamics. We demonstrate that stimulus-stimulus-response as- sociation can be implemented in a stochastic way within a noisy setting. The network has rich dynamics resulting from its recurrent connectiv- ity and background activity. The algorithm can learn temporal sequence detection and solve temporal XOR problem
Integration of Static and Self-motion-Based Depth Cues for Efficient Reaching and Locomotor Actions

The common approach to estimate the distance of an object in computer vision and robotics is to use stereo vision. Stereopsis, however, provides good estimates only within near space and thus is more suitable for reaching actions. In order to successfully plan and execute an action in far space, other depth cues must be taken into account. Self-body movements, such as head and eye movements or locomotion can provide rich information of depth. This paper proposes a model for integration of static and self-motion-based depth cues for a humanoid robot. Our results show that self-motion-based visual cues improve the accuracy of distance perception and combined with other depth cues provide the robot with a robust distance estimator suitable for both reaching and walking actions
Contour detection by CORF operator

We propose a contour operator, called CORF, inspired by\ud
the properties of simple cells in visual cortex. It combines, by a weighted\ud
geometric mean, the blurred responses of difference-of-Gaussian operators\ud
that model cells in the lateral geniculate nucleus (LGN). An operator\ud
that has gained particular popularity as a computational model of a simple\ud
cell is based on a family of Gabor Functions (GFs). However, the GF\ud
operator short-cuts the LGN, and its effectiveness in contour detection\ud
tasks, which is assumed to be the primary biological role of simple cells,\ud
has never been compared with the effectiveness of alternative operators.\ud
We compare the performances of the CORF and the GF operators using\ud
the RuG and the Berkeley data sets of natural scenes with associated\ud
ground truths. The proposed CORF operator outperforms the GF operator\ud
(RuG: t(39)=4.39, p<10−4 and Berkeley: t(499)=4.95, p<10−6).peer-reviewe
Optimal “anti-Bayesian” parametric pattern classification using Order Statistics criteria

Published version of a chapter in the book: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. Also available from the publisher at: http://dx.doi.org/10.1007/978-3-642-33275-3_1The gold standard for a classifier is the condition of optimality attained by the Bayesian classifier. Within a Bayesian paradigm, if we are allowed to compare the testing sample with only a single point in the feature space from each class, the optimal Bayesian strategy would be to achieve this based on the (Mahalanobis) distance from the corresponding means. The reader should observe that, in this context, the mean, in one sense, is the most central point in the respective distribution. In this paper, we shall show that we can obtain optimal results by operating in a diametrically opposite way, i.e., a so-called “anti-Bayesian” manner. Indeed, we shall show the completely counter-intuitive result that by working with a very few (sometimes as small as two) points distant from the mean, one can obtain remarkable classification accuracies. Further, if these points are determined by the Order Statistics of the distributions, the accuracy of our method, referred to as Classification by Moments of Order Statistics (CMOS), attains the optimal Bayes’ bound! This claim, which is totally counter-intuitive, has been proven for many uni-dimensional, and some multi-dimensional distributions within the exponential family, and the theoretical results have been verified by rigorous experimental testing. Apart from the fact that these results are quite fascinating and pioneering in their own right, they also give a theoretical foundation for the families of Border Identification (BI) algorithms reported in the literature
On the vulnerability of iris-based systems to a software attack based on a genetic algorithm

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-33275-3_14Proceedings of 17th Iberoamerican Congress, CIARP 2012, Buenos Aires, ArgentinaThe vulnerabilities of a standard iris verification system to a novel indirect attack based on a binary genetic algorithm are studied. The experiments are carried out on the iris subcorpus of the publicly available BioSecure DB. The attack has shown a remarkable performance, thus proving the lack of robustness of the tested system to this type of threat. Furthermore, the consistency of the bits of the iris code is analysed, and a second working scenario discarding the fragile bits is then tested as a possible countermeasure against the proposed attack.This work has been partially supported by projects Contexts (S2009/TIC-1485) from CAM, Bio-Challenge (TEC2009-11186) from Spanish MICINN, TABULA RASA (FP7-ICT-257289) and BEAT (FP7-SEC-284989) from EU, and Cátedra UAM-Telefónica
The Intrinsic Dimensionality of Attractiveness: A Study in Face Profiles

The study of human attractiveness with pattern analysis techniques is an emerging research field. One still largely unresolved problem is which are the facial features relevant to attractiveness, how they combine together, and the number of independent parameters required for describing and identifying harmonious faces. In this paper, we present a first study about this problem, applied to face profiles. First, according to several empirical results, we hypothesize the existence of two well separated manifolds of attractive and unattractive face profiles. Then, we analyze with manifold learning techniques their intrinsic dimensionality. Finally, we show that the profile data can be reduced, with various techniques, to the intrinsic dimensions, largely without loosing their ability to discriminate between attractive and unattractive face
Exploiting the social and semantic web for guided web archiving

The constantly growing amount of Web content and the success of the Social Web lead to increasing needs for Web archiving. These needs go beyond the pure preservation of Web pages. Web archives are turning into "community memories" that aim at building a better understanding of the public view on, e.g., celebrities, court decisions, and other events. In this paper we present the ARCOMEM architecture that uses semantic information such as entities, topics, and events complemented with information from the social Web to guide a novel Web crawler. The resulting archives are automatically enriched with semantic meta-information to ease the access and allow retrieval based on conditions that involve high-level concepts. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-33290-6_47.German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety/0325296Solland Solar Cells BVSolarWorld Innovations GmbHSCHOTT Solar AGRENA GmbHSINGULUS TECHNOLOGIES A
Polynomial time and parameterized approximation algorithms for boxicity

The boxicity (cubicity) of a graph G, denoted by box(G) (respectively cub(G)), is the minimum integer k such that G can be represented as the intersection graph of axis parallel boxes (cubes) in ℝ k . The problem of computing boxicity (cubicity) is known to be inapproximable in polynomial time even for graph classes like bipartite, co-bipartite and split graphs, within an O(n 0.5 − ε ) factor for any ε > 0, unless NP = ZPP.\ud
\ud
We prove that if a graph G on n vertices has a clique on n − k vertices, then box(G) can be computed in time n22O(k2logk) . Using this fact, various FPT approximation algorithms for boxicity are derived. The parameter used is the vertex (or edge) edit distance of the input graph from certain graph families of bounded boxicity - like interval graphs and planar graphs. Using the same fact, we also derive an O(nloglogn√logn√) factor approximation algorithm for computing boxicity, which, to our knowledge, is the first o(n) factor approximation algorithm for the problem. We also present an FPT approximation algorithm for computing the cubicity of graphs, with vertex cover number as the parameter
Relations as executable specifications: taming partiality and non-determinism using invariants

Comunicação  publicada em "Lecture Notes in Computer Science", vol. 7560 (2012), pag. 146-161The calculus of relations has been widely used in program specification and reasoning. It is very tempting to use such specifications as running prototypes of the desired program, but, even considering finite domains, the inherent partiality and non-determinism of relations makes this impractical and highly inefficient. To tame partiality we prescribe the usage of invariants, represented by coreflexives, to characterize the exact domains and codomains of relational specifications. Such invariants can be used as pre-condition checkers to avoid runtime errors. Moreover, we show how such invariants can be used to narrow the non-deterministic execution of relational specifications, making it viable for a relevant class of problems. In particular, we show how the proposed techniques can be applied to execute specifications of bidirectional transformations, a domain where partiality and non-determinism are paramount.Fundação para a Ciência e a Tecnologi
Exploring micromovements with motion capture and sonification

We present the results of a pilot study on how micromovements may be used in an interactive dance/music performance. Micromovements are subtle body movements that cannot be easily seen by the human eye. Using an infrared marker-based motion capture system we have explored micromovements through 15x10 minutes long observation studies of ourselves standing still or moving slowly. The results from these studies show that there are both consistent and inconsistent movement patterns to be found at various temporal levels. Experimentation with three different types of realtime sonification shows artistic potential in using micromovements in dance and music performance.

Author-prepared version, paper presented at: ArtsIT 2011 - Second International ICST Conference on Arts and Technology Copyright ICST. The definitive version is available at springerlink.co
Text invader: a graphic interference on semantic flow

In this paper we report on a system of typographic intervention which aims to bring about a typesetting environment which automates the aesthetic as well as contextual concerns previously manifested in the output of deconstructivist typographers throughout the 20th century. While in the current state of the undertaking a commercial font design software named FontLab has been used to bring about a virus which substitutes vectors for semantic patterns found in bodies of text, future work will evolve towards the creation of a standalone application which may aid in a convergence of the fields of textual authorship and graphic design
ICT and environmental sustainability : a case study of a grassroots initiative

Increasingly, local communities develop projects and grassroots
initiatives that address climate change and other environmental sustainability
issues. These projects often commence informally and adopt web-based free
information and communication technologies (ICT) applications to support
their functioning. ICT applications are used to promote the goals of the project,
to recruit more supporters and to facilitate debate among citizens sympathetic to
the cause of environmental stewardship. However, as some of these projects
evolve they become more complex. Struggling with lack of funding, the
solution is to imaginatively combine free web-based ICT applications, to adapt
existing open source applications, and even to develop customized solutions to
address the project’s needs. We present the results of an exploratory case study
of a grassroots initiative with environmental sustainability goals. The study
shows some evidence of innovative practices in the appropriation of ICTs and
in the communication campaign. Implications of this research for online
communities and society are also discussed.Fundação para a Ciência e a Tecnologia (FCT
Representing uncertainty by possibility distributions encoding confidence bands, tolerance and prediction intervals

For a given sample set, there are already different methods for building possibility distributions encoding the family of probability distributions that may have generated the sample set. Almost all the existing methods are based on parametric and distribution free confidence bands. In this work, we introduce some new possibility distributions which encode different kinds of uncertainties not treated before. Our possibility distributions encode statistical tolerance and prediction intervals (regions). We also propose a possibility distribution encoding the confidence band of the normal distribution which improves the existing one for all sample sizes. In this work we keep the idea of building possibility distributions based on intervals which are among the smallest intervals for small sample sizes. We also discuss the properties of the mentioned possibility distributions
From imprecise probability laws to fault tree analysis

Reliability studies and system's health predictions are mostly based on the use of probability laws to model the failure of components. Behavior of the components of the system under study is represented by probability distributions, derived from failure statistics. The parameters of these laws are assumed to be precise and well known, which is not always true in practice. Impact of such imprecision on the end result can be crucial, and requires adequate sensitivity analysis. One way to tackle this imprecision is to bound such parameters within an interval. This paper investigates the impact of the uncertainty in the values of law parameters, specifically in fault tree based Safety analysis
The Contributions of Alessandro D’Atri to Organization and Information Systems Studies

This book is dedicated to the memory of Professor Alessandro (Sandro) D'Atri, who passed away in April 2011. Professor D'Atri started his career as a brilliant scholar interested in theoretical computer science, databases and, more generally information processing systems. He journeyed far in various applications, such as human-computer interaction, human factors, ultimately arriving at business information systems and business organisation after more than 20 years of researc hbased on "problem solving". Professor D'Atri pursued the development of an interdisciplinary culture in which social sciences, systems design and human sciences are mutually integrated. Rather than retrospection, this book is aimed to advance in these directions and to stimulate a debate about the potential of design research in the field of information systems and organisation studies with an interdisciplinary approach. Each chapter has been selected by the Editorial Board following a double blind peer review process. The general criteria of privileging the variety of topics and the design science orientation and/or empirical works in which a design research approach is adopted to solve various field problems in the management area. In addition several chapters contribute to the meta-discourse on design science research.This book is dedicated to the memory of Professor Alessandro (Sandro) D'Atri, who passed away in April 2011. Professor D'Atri started his career as a brilliant scholar interested in theoretical computer science, databases and, more generally information processing systems. He journeyed far in various applications, such as human-computer interaction, human factors, ultimately arriving at business information systems and business organisation after more than 20 years of researc hbased on "problem solving". Professor D'Atri pursued the development of an interdisciplinary culture in which social sciences, systems design and human sciences are mutually integrated. Rather than retrospection, this book is aimed to advance in these directions and to stimulate a debate about the potential of design research in the field of information systems and organisation studies with an interdisciplinary approach. Each chapter has been selected by the Editorial Board following a double blind peer review process. The general criteria of privileging the variety of topics and the design science orientation and/or empirical works in which a design research approach is adopted to solve various field problems in the management area. In addition several chapters contribute to the meta-discourse on design science research.Monograph's chapter
Models of rotating massive stars: impacts of various prescriptions

The rotation of stars has many interesting and important consequences for the
photometric and chemical evolution of galaxies. Many of the predictions of
models of stellar rotation are now compared with observations of surface
abundances and velocities, with interferometric studies of fast rotating stars,
with internal rotation profiles as they can be deduced by asteroseismology, to
cite just a few observational constraints. In this paper, we investigate how
the outputs of models depend on the prescriptions used for the diffusion
coefficients included in the shellular rotating models. After recalling the
various prescriptions found in the literature, we discuss their impacts on the
evolutionary tracks and lifetimes of the Main-Sequence (MS) phase, the changes
of the surface composition and velocities during the MS phase, the distribution
of the core helium lifetime in the blue and the red part of the HR diagram, the
extensions of the blue loops, the evolution of the angular momentum of the
core, and the synthesis of primary nitrogen in fast-rotating metal-poor massive
stars. While some of these outputs depend only slightly on the prescriptions
used (for instance, the evolution of the surface velocities), most of them show
a significant dependence. The models which best fit the changes of the surface
abundances are those computed with the vertical shear diffusion coefficient of
Maeder (1997) and the horizontal shear diffusion coefficient by Zahn (1992).Comment: 19 pages, 4 figures, to appear in "SeIsmology for studies of stellar
  Rotation and Convection", Lecture Notes in Physics (Springer Verlag, Berlin),
  M.J. Goupil, K. Belkacem, C. Neiner, F. Ligni\`eres, and J. J. Green (eds
Semi-convection: What is the underlying physical context?

Stellar conditions leading to a possible semi-convective mixing are discussed
in three relevant cases: (1) low mass MS stars in which the CNO cycle takes
progressively the lead over the PP chain due to the increase in temperature as
core hydrogen burning proceeds, (2) massive MS stars which experience a large
contri- bution of the radiation pressure to the total pressure and (3) core
helium burning stars for which the production of carbon in the core increases
the opacity. A short discussion of semi-convection in terms of instability of
non radial modes follows.Comment: 12 pages, 6 figures, in "Studying stellar Rotation and Convection",
  Lecture Notes in Physics (Springer Verlag, Berlin), M.J. Goupil, K. Belkacem,
  C. Neiner, F. Ligni\`eres, and J. J. Green (eds
Ab initio modelling of steady rotating stars

Modelling isolated rotating stars at any rotation rate is a challenge for the
next generation of stellar models. These models will couple dynamical aspects
of rotating stars, like angular momentum and chemicals transport, with
classical chemical evolution, gravitational contraction or mass-loss. Such
modelling needs to be achieved in two dimensions, combining the calculation of
the structure of the star, its mean flows and the time-evolution of the whole.
We present here a first step in this challenging programme. It leads to the
first self-consistent two-dimensional models of rotating stars in a steady
state generated by the ESTER code. In these models the structure (pressure,
density and temperature) and the flow fields are computed in a self-consistent
way allowing the prediction of the differential rotation and the associated
meridian circulation of the stars. After a presentation of the physical
properties of such models and the numerical methods at work, we give the first
grid of such models describing massive and intermediate-mass stars for a
selection of rotation rates up to 90% of the breakup angular velocity.Comment: 25 pages, 6 figures, Contribution to the book "Seismology for studies
  of stellar Rotation and Convection", Edts Goupil et al., to be published in
  Lecture Notes in Physics, Springe