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Yakovlev, Victor; Korzhik, Valery; Bakaev, Mihail; MoralesLuna, Guillermo
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1 Citations
We consider the informationtheoretic secure key distribution problem (KDP) over noisy binary symmetric channels with public discussion and in the presence of an active adversary. There are several versions of such protocols proposed by Maurer, Wolf, Renner, Dodis, Reyzin et al. We describe two new versions of KDP for the same channel model and with the use of extractors as a mean of privacy amplification but with the goal to maximize the key rate under an optimization of the protocol parameters. There are two novelties in solution of KDP: we get the extractor’s seed directly from the distributed initial strings and we prove the main results in terms of explicit estimates without the use of the uncertain symbols O, Ω, Θ. Both asymptotic and nonasymptotic cases are presented. It is shown that the extractors can be superior to conventional hashing for very large lengths of initially distributed strings.
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By
FuentesCastañeda, Laura; Knapp, Edward; RodríguezHenríquez, Francisco
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7 Citations
An asymmetric pairing
$e\colon{\mathbb{G}}_2\times{\mathbb{G}}_1\to{\mathbb{G}}_T$
is considered such that
${\mathbb{G}}_1=E({\mathbb F}_p)[r]$
and
${\mathbb{G}}_2=\tilde E({\mathbb F}_{p^{k/d}})[r]$
, where k is the embedding degree of the elliptic curve
$E/{\mathbb F}_p$
, r is a large prime divisor of
$\# E({\mathbb F}_p)$
, and
$\tilde E$
is the degreed twist of E over
${\mathbb F}_{p^{k/d}}$
with
$r \mid \tilde E ({\mathbb F}_{p^{k/d}} )$
. Hashing to
${\mathbb{G}}_1$
is considered easy, while hashing to
${\mathbb{G}}_2$
is done by selecting a random point Q in
$\tilde E({\mathbb F}_{p^{k/d}})$
and computing the hash value cQ, where c·r is the order of
$\tilde E({\mathbb F}_{p^{k/d}})$
. We show that for a large class of curves, one can hash to
${\mathbb{G}}_2$
in
$\textup{O}(1/\varphi (k)\log c)$
time, as compared with the previously fastestknown
$\textup{O}(\log p)$
. In the case of BN curves, we are able to double the speed of hashing to
${\mathbb{G}}_2$
. For higherembeddingdegree curves, the results can be more dramatic. We also show how to reduce the cost of the finalexponentiation step in a pairing calculation by a fixed number of field multiplications.
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By
Castañeda, Armando; Imbs, Damien; Rajsbaum, Sergio; Raynal, Michel
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1 Citations
In the waitfree shared memory model substantial attention has been devoted to understanding the relative power of subconsensus tasks. Two important subconsensus families of tasks have been identified: kset agreement and Mrenaming. When 2 ≤ k ≤ n − 1 and n ≤ M ≤ 2n − 2, these tasks are more powerful than read/write registers, but not strong enough to solve consensus for two processes.
This paper studies the power of renaming with respect to set agreement. It shows that, in a system of n processes, nrenaming is strictly stronger than (n − 1)set agreement, but not stronger than (n − 2)set agreement. Furthermore, (n + 1)renaming cannot solve even (n − 1)set agreement. As a consequence, there are cases where set agreement and renaming are incomparable when looking at their power to implement each other.
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By
Aranha, Diego F.; FazHernández, Armando; López, Julio; RodríguezHenríquez, Francisco
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8 Citations
We design a stateoftheart software implementation of field and elliptic curve arithmetic in standard Koblitz curves at the 128bit security level. Field arithmetic is carefully crafted by using the best formulae and implementation strategies available, and the increasingly common native support to binary field arithmetic in modern desktop computing platforms. The ith power of the Frobenius automorphism on Koblitz curves is exploited to obtain new and faster interleaved versions of the wellknown τNAF scalar multiplication algorithm. The usage of the
$\tau^{\lfloor m/3 \rfloor}$
and
$\tau^{\lfloor m/4 \rfloor}$
maps are employed to create analogues of the 3and 4dimensional GLV decompositions and in general, the
$\lfloor m/s \rfloor$
th power of the Frobenius automorphism is applied as an analogue of an sdimensional GLV decomposition. The effectiveness of these techniques is illustrated by timing the scalar multiplication operation for fixed, random and multiple points. In particular, our library is able to compute a random point scalar multiplication in just below 10^{5} clock cycles, which sets a new speed record across all curves with or without endomorphisms defined over binary or prime fields. The results of our optimized implementation suggest a tradeoff between speed, compliance with the published standards and sidechannel protection. Finally, we estimate the performance of curvebased cryptographic protocols instantiated using the proposed techniques and compare our results to related work.
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By
MartínezHerrera, Alberto F.; MexPerera, J. Carlos; NolazcoFlores, Juan A.
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Substitution Box (Sbox) is usually the most complex module in some block ciphers. Some prominent ciphers such as AES and Camellia use Sboxes, which are affine equivalents of a multiplicative inverse in small finite fields. This manuscript describes mathematical representations of the Camellia Sbox by using composite fields such as polynomial, normal or mixed. An optimized hardware implementation typically aims to reduce the number of gates to be used. Our theoretical design with composite normal bases allows saving gates in the critical path by using 19 XOR gates, 4 AND gates and 2 NOT gates. With composite mixed bases, the critical path has 2 XOR gates more than the representation with composite normal bases. Redundancies found in the affine transformation matrix that form the composite fields were eliminated. For mixed bases, new Algebraic Normal Form identities were obtained to compute the inner composite multiplicative inverse, reducing the critical path of the complete implementation of the Camellia Sbox. These constructions were translated into transistorgate architectures for hardware representations by using Electric VLSI [29] under MOSIS C5 process [17], [18], thus obtaining the corresponding schematic models.
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By
GarzaFabre, Mario; RodriguezTello, Eduardo; ToscanoPulido, Gregorio
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2 Citations
Through multiobjectivization, a singleobjective problem is restated in multiobjective form with the aim of enabling a more efficient search process. Recently, this transformation was applied with success to the hydrophobicpolar (HP) lattice model, which is an abstract representation of the protein structure prediction problem. The use of alternative multiobjective formulations of the problem has led to significantly better results. In this paper, an improved multiobjectivization for the HP model is proposed. By decomposing the HP model’s energy function, a twoobjective formulation for the problem is defined. A comparative analysis reveals that the new proposed multiobjectivization evaluates favorably with respect to both the conventional singleobjective and the previously reported multiobjective formulations. Statistical significance testing and the use of a large set of test cases support the findings of this study. Both twodimensional and threedimensional lattices are considered.
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By
Confalonieri, Roberto; Nieves, Juan Carlos; Osorio, Mauricio; VázquezSalceda, Javier
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4 Citations
In this paper, we show how the formalism of Logic Programs with Ordered Disjunction (LPODs) and Possibilistic Answer Set Programming (PASP) can be merged into the single framework of Logic Programs with Possibilistic Ordered Disjunction (LPPODs). The LPPODs framework embeds in a unified way several aspects of commonsense reasoning, nonmonotonocity, preferences, and uncertainty, where each part is underpinned by a well established formalism. On one hand, from LPODs it inherits the distinctive feature of expressing contextdependent qualitative preferences among different alternatives (modeled as the atoms of a logic program). On the other hand, PASP allows for qualitative certainty statements about the rules themselves (modeled as necessity values according to possibilistic logic) to be captured. In this way, the LPPODs framework supports a reasoning which is nonmonotonic, preference and uncertaintyaware. The LPPODs syntax allows for the specification of (1) preferences among the exceptions to default rules, and (2) necessity values about the certainty of program rules. As a result, preferences and uncertainty can be used to select the preferred uncertain default rules of an LPPOD and, consequently, to order its possibilistic answer sets. Furthermore, we describe the implementation of an ASPbased solver able to compute the LPPODs semantics.
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By
GarzaFabre, Mario; RodriguezTello, Eduardo; ToscanoPulido, Gregorio
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3 Citations
The hydrophobicpolar (HP) model for protein structure prediction abstracts the fact that hydrophobic interactions are a dominant force in the protein folding process. This model represents a hard combinatorial optimization problem, which has been widely addressed using evolutionary algorithms and other metaheuristics. In this paper, the multiobjectivization of the HP model is proposed. This originally singleobjective problem is restated as a multiobjective one by decomposing the conventional objective function into two independent objectives. By using different evolutionary algorithms and a large set of test cases, the new alternative formulation was compared against the conventional singleobjective problem formulation. As a result, the proposed formulation increased the search performance of the implemented algorithms in most of the cases. Both two and threedimensional lattices are considered. To the best of authors’ knowledge, this is the first study where multiobjective optimization methods are used for solving the HP model.
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By
Pierrard, Thomas; Coello Coello, Carlos A.
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3 Citations
This paper presents a new artificial immune system algorithm for solving multiobjective optimization problems, based on the clonal selection principle and the hypervolume contribution. The main aim of this work is to investigate the performance of this class of algorithm with respect to approaches which are representative of the stateoftheart in multiobjective optimization using metaheuristics. The results obtained by our proposed approach, called multiobjective artificial immune system based on hypervolume (MOAISHV) are compared with respect to those of the NSGAII. Our preliminary results indicate that our proposed approach is very competitive, and can be a viable choice for solving multiobjective optimization problems.
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By
Baldwin, Brian; Goundar, Raveen R.; Hamilton, Mark; Marnane, William P.
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12 Citations
Recent elliptic curve scalar multiplication algorithms are based on efficient co
$$Z$$
arithmetics. These arithmetics were initially introduced by Meloni in 2007 where addition of projective points share the same
$$Z$$
coordinate. The co
$$Z$$
version algorithms are sufficiently fast and secure against a large variety of implementation attacks. This paper analyses the performance of these algorithms in hardware and then compares them against software and hardware–software codesign environments on FPGA, in terms of speed, memory, power and energy consumption. Specifically, this paper presents a survey and performance comparison of implementations of co
$$Z$$
versions of the Montgomery ladder and the Joye’s doubleadd algorithm in an embedded system environment.
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By
Alférez, Germán H.; Pelechano, Vicente
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6 Citations
Modeldriven techniques have proven to yield significant benefits for contextaware systems. Specifically, semanticallyrich models are used at runtime to monitor the system context and guide necessary changes. Under the closedworld assumption, adaptations are fully known at design time. Nevertheless, it is difficult to foresee all the possible situations that may arise in uncertain and complex contexts. In this paper, we present a modelbased framework to support the dynamic evolution of contextaware systems to deal with unexpected context events in the open world. If model adaptations are not enough to solve uncertainty, our modelbased evolution planner guides the evolution of the supporting models to preserve highlevel requirements. A case study about a contextaware Web service composition, which is executed in a distributed computing infrastructure, illustrates the applicability of our framework. A realization methodology and a prototype system support our approach.
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By
RosalesPérez, Alejandro; ReyesGarcía, Carlos A.; Gonzalez, Jesus A.; ArchTirado, Emilio
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2 Citations
In the last years, infant cry recognition has been of particular interest because it contains useful information to determine if the infant is hungry, has pain, or a particular disease. Several studies have been performed in order to differentiate between these kinds of cries. In this work, we propose to use Genetic Selection of a Fuzzy Model (GSFM) for classification of infant cry. GSFM selects a combination of feature selection methods, type of fuzzy processing, learning algorithm, and its associated parameters that best fit to the data. The experiments demonstrate the feasibility of this technique in the classification task. Our experimental results reach up to 99.42% accuracy.
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By
Castañeda, Armando; Herlihy, Maurice; Rajsbaum, Sergio
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3 Citations
In the renaming problem, each process in a distributed system is issued a unique name from a large name space, and the processes must coordinate with one another to choose unique names from a much smaller name space.
We show that lower bounds on the solvability of renaming in an asynchronous distributed system can be formulated as a purely topological question about the existence of an equivariant chain map from a “topological disk” to a “topological annulus”. Proving the nonexistence of such a map implies the nonexistence of a distributed renaming algorithm in several related models of computation.
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By
Sossa, Humberto; Garro, Beatriz A.; Villegas, Juan; Avilés, Carlos; Olague, Gustavo
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In this note we present our most recent advances in the automatic design of artificial neural networks (ANNs) and associative memories (AMs) for pattern classification and pattern recall. Particle Swarm Optimization (PSO), Differential Evolution (DE), and Artificial Bee Colony (ABC) algorithms are used for ANNs; Genetic Programming is adopted for AMs. The derived ANNs and AMs are tested with several examples of wellknown databases. As we will show, results are very promising.
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By
GarciaNajera, Abel
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2 Citations
In the Vehicle Routing Problem with Backhauls there are linehaul customers, who demand products, and backhaul customers, who supply products, and there is a fleet of vehicles available for servicing customers. The problem consists in finding a set of routes with the minimum cost, such that all customers are serviced. A generalization of this problem considers the collection from the backhaul customers optional. If the number of vehicles, the cost, and the uncollected demand are assumed to be equally important objectives, the problem can be tackled as a multiobjective optimization problem. In this paper, we solve these as multiobjective problems with an adapted previously proposed evolutionary algorithm and evaluate its performance with proper tools.
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By
Ita Luna, Guillermo; MarcialRomero, J. Raymundo
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1 Citations
We present some results about the parametric complexity of #2SAT and #2UNSAT, which consist on counting the number of models and falsifying assignments, respectively, for two Conjunctive Forms (2CF’s) . Firstly, we show some cases where given a formula F, #2SAT(F) can be bounded above by considering a binary pattern analysis over its set of clauses. Secondly, since #2SAT(F) = 2^{n}#2UNSAT(F) we show that, by considering the constrained graph G_{F} of F, if G_{F} represents an acyclic graph then, #UNSAT(F) can be computed in polynomial time. To the best of our knowledge, this is the first time where #2UNSAT is computed through its constrained graph, since the inclusionexclusion formula has been commonly used for computing #UNSAT(F).
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By
TecuanhuehueVera, P.; CarrascoOchoa, Jesús Ariel; MartínezTrinidad, José Fco.
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Multidimensional scaling maps a set of ndimensional objects into a lowerdimension space, usually the Euclidean plane, preserving the distances among objects in the original space. Most algorithms for multidimensional scaling have been designed to work on numerical data, but in soft sciences, it is common that objects are described using quantitative and qualitative attributes, even with some missing values. For this reason, in this paper we propose a genetic algorithm especially designed for multidimensional scaling over mixed and incomplete data. Some experiments using datasets from the UCI repository, and a comparison against a common algorithm for multidimensional scaling, shows the behavior of our proposal.
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By
Urzaiz, Gabriel; Hervás, Ramón; Villarreal, Vladimir; Bravo, José
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An innovative communication platform is presented to enable collaborative tourism applications. The user information is constructed not only from a local application or from a remote server, but also with much more information coming from other network participants. The solution is based on an overlay network that is constructed by means of an objectoriented middleware for distributed applications, enabling the collaboration of several end users and servers. A software prototype was developed to implement a proof of concept in order to demonstrate feasibility.
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By
MedinaPérez, Miguel Angel; GarcíaBorroto, Milton; GutierrezRodríguez, Andres Eduardo; AltamiranoRobles, Leopoldo
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1 Citations
Developing accurate fingerprint verification algorithms is an active research area. A large amount of fingerprint verification algorithms are based on minutiae descriptors. An important component of these algorithms is the alignment strategy. The single alignment strategy, with O(n^{2}) time complexity, uses the local matching minutiae pair that maximizes the similarity value to align the minutiae. Nevertheless, even if the selected minutiae pair is a true matching pair, it is not necessarily the best pair to carry out fingerprint alignment. The multiple alignments strategy alleviates these limitations by performing multiple minutiae alignments, increasing the time complexity to O(n^{4}). In this paper, we improve the multiple alignment strategy, reducing its complexity while still achieving a high accuracy. The new strategy is based on the rationale that most minutiae descriptors from one fingerprint correspond with their most similar descriptors from the other fingerprint. To test the new strategy behavior, we adapt three well known algorithms to a traditional multiple alignment strategy and to our strategy. Several experiments in the FVC2004 database show that our strategy outperforms both the single and the multiple alignments strategies.
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By
Arriaga, Jonathan; ValenzuelaRendón, Manuel
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1 Citations
The construction of a portfolio in the financial field is a problem faced by individuals and institutions worldwide. In this paper we present an approach to solve the portfolio selection problem with the Steepest Ascent Hill Climbing algorithm. There are many works reported in the literature that attempt to solve this problem using evolutionary methods. We analyze the quality of the solutions found by a simpler algorithm and show that its performance is similar to a Genetic Algorithm, a more complex method. Real world restrictions such as portfolio value and rounded lots are considered to give a realistic approach to the problem.
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Retchkiman Konigsberg, Zvi
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This paper addresses the biological interaction stability problem among organisms of the same or different species associated with the need for a common resource that occurs in a limited supply relative to demand by considering it as a discrete event dynamical system. Timed Petri nets are a graphical and mathematical modeling tool applicable to discrete event dynamical systems in order to represent its states evolution. Lyapunov stability theory provides the required tools needed to aboard the stability problem for discrete event dynamical systems modeled with timed Petri nets. By proving boundedness one confirms a dominant oscillating behavior of both organisms dynamics performance. However, the oscillating frequency results to be unknown. This inconvenience is overcome by considering a specific recurrence equation, in the maxplus algebra.
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By
Son, Tran Cao; Pontelli, Enrico; Sakama, Chiaki
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This paper investigates the use of highlevel action languages for representing and reasoning about commitments in multiagent domains. We introduce the language
${\cal L}^{mt}$
, an extension of the language
$\cal L$
, with new features motivated by the problem of representing and reasoning about commitments. The paper demonstrates how features and properties of commitments can be described in this action language. We show how
${\cal L}^{mt}$
can handle both simple commitment actions as well as complex commitment protocols. Furthermore, the semantics of
${\cal L}^{mt}$
provides a uniform solution to different problems in reasoning about commitments such as the problem of (i) verifying whether an agent fails (or succeeds) to deliver on its commitments; (ii) identifying pending commitments; and (iii) suggesting ways to satisfy pending commitments.
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By
PeñaAyala, Alejandro; Mizoguchi, Riichiro
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1 Citations
Our intelligent decisionmaking approach (IDMA) is an instance of cognitive computing. It applies causality as common sense reasoning and fuzzy logic as a representation for qualitative knowledge. Our IDMA collects raw knowledge of humans through psychological models to tailor a knowledgebase (KB). The KB manages different repositories (e.g., cognitive maps (CM) and an ontology) to depict the object of study. The IDMA traces fuzzycausal inferences to simulate causal behavior and estimate causal outcomes for decisionmaking. In order to test our approach, it is linked to the sequencing module of an intelligent and adaptive webbased educational system (IAWBES). It is used to provide studentcentered education and enhance the students’ learning by intelligent and adaptive functionalities. The results reveal users of an experimental group reached 17% of better learning than their peers of the control group.
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By
MoralesGonzález, Annette; GarcíaReyes, Edel; Sucar, Luis Enrique
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1 Citations
Image segmentation and Automatic Image Annotation (AIA) are two important areas that still impose challenging problems. Addressing both problems simultaneously may improve their results since they are interdependent. In this paper we give a step ahead in that direction considering different segmentation levels simultaneously and possible contextual relations among segments in order to improve the automatic image annotation. We propose to include hierarchical relations among regions of an image in a Markov Random Field (MRF) model for annotation. This relations are obtained from irregular pyramids, which keep parentchild relations among regions through all the levels. Our main contribution is therefore the combination of the irregular pyramid approach with context modeling by means of hierarchical MRFs. Experiments run in a subset of the Corel image collection showed a relevant improvement in the annotation accuracy.
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By
Aguilar Calderon, José Alfonso; Garrigós, Irene; Casteleyn, Sven; Mazón, JoseNorberto
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In this paper we present the WebREdTool, a set of Eclipse plugins that have been developed to assist the designer in the early phases of a Web application development process. With the WebREdTool, the designer can specify the Web application requirements by using the i* goaloriented framework. The WebREdTool assists the designer to compare different configurations of functional requirements, while balancing and optimizing nonfunctional requirements. The underlying algorithm to support this is based on the Pareto efficiency, but to help the designer to better assess and compare each configuration, the WebREdTool is also able to visualize each configurations using a radarchart.
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By
Tanveer, Saad; Muhammad, Aslam; MartinezEnriquez, A. M.; EscaladaImaz, G.
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Languages like Spanish and Arabic are spoken over a large geographic area. The people that speak these languages develop differences in accent, annotation and phonetic delivery. This leads to difficulty in standardization of languages for education and communication (both text and oral). The problem is addressed by phonetic dictionaries to some extent. They provide the correct pronunciation for a word. But, they contribute little to standardize or unify the language for a learner. Our system is to provide unification of different accents and dialects. It creates a standard for learning and communication.
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By
MartínezVillaseñor, María de Lourdes Guadalupe; GonzálezMendoza, Miguel
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A major challenge for creating personalized diet and activity applications is to capture static, semistatic and dynamic information about a person in a userfriendly way. Sharing and reusing information between heterogeneous sources like social networking applications, personal health records, specialized applications for diet and exercise monitoring, and personal devices with attached sensors can achieve a better understanding of the user. But gathering distributed user information from heterogeneous sources and making sense of it to enable user model interoperability entails handling the semantic heterogeneity of the user models. In this paper we describe a flexible user modeling ontology to provide representation for a ubiquitous user model and a process of concept alignment for interoperability between heterogeneous sources to address the lack of interoperability between profile suppliers and consumers. We provide an example of how information of different profile suppliers can be used to enrich fitness applications and personalize web services.
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By
AvilaGeorge, Himer; TorresJimenez, Jose; RangelValdez, Nelson; Carrión, Abel; Hernández, Vicente
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7 Citations
The Covering Arrays (CAs) are mathematical objects with minimal coverage and maximum cardinality that are a good tool for the design of experiments. A covering array is an N×k matrix over an alphabet v s.t. each N×k subset contains at least one time each combination from {0,1,…,v−1}^{t}, given a positive integer value t. The process of ensuring that a CA contains each of the v^{t} combinations is called verification of CA. In this paper, we present an algorithm for CA verification and its implementation details in three different computation paradigms: (a) sequential approach (SA); (b) parallel approach (PA); and (c) Grid approach (GA). Four different PAs were compared in their performance of verifying a matrix as a CA; the PA with the best performance was included in a different experimentation where the three paradigms, SA, PA, and GA were compared in a benchmark composed by 45 possible CA instances. The results showed the limitations of the different paradigms when solving the verification of CA problem, and points out the necessity of a Grid approach to solve the problem when the size of a CA grows.
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By
Alajarmeh, Nancy; Pontelli, Enrico
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2 Citations
In this paper we describe a multilayer system that is designed to help students who have moderate to severe visual impairments learn algebra while manipulating algebraic equations through an interactive nonvisual webbased workspace. The functional algebraic transformation options provided in the interactive system through its various layers, and the carefully provided help associated to each of those domain specific manipulation functions enhanced the overall process by which students who are visually impaired learn and deal with solving equations in the developed nonvisual workspace.
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By
Batyrshin, Ildar
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4 Citations
The method of recognition of shape association patterns with direct and inverse relationships is proposed. This method is based on a new time series shape association measure based on Up and Down trend associations. The application of this technique to analysis of associations between well production data in petroleum reservoirs is discussed.
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By
Aichholzer, Oswin; Cetina, Mario; FabilaMonroy, Ruy; Leaños, Jesús; Salazar, Gelasio; Urrutia, Jorge
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Let P be a simple polygon on the plane. Two vertices of P are visible if the open line segment joining them is contained in the interior of P. In this paper we study the following questions posed in [8,9]: (1) Is it true that every nonconvex simple polygon has a vertex that can be continuously moved such that during the process no vertexvertex visibility is lost and some vertexvertex visibility is gained? (2) Can every simple polygon be convexified by continuously moving only one vertex at a time without losing any internal vertexvertex visibility during the process?
We provide a counterexample to (1). We note that our counterexample uses a monotone polygon. We also show that question (2) has a positive answer for monotone polygons.
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By
Escalante, Hugo Jair; Montes, Manuel; Sucar, Enrique
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9 Citations
This paper introduces two novel strategies for representing multimodal images with application to multimedia image retrieval. We consider images that are composed of both text and labels: while text describes the image content at a very high semantic level (e.g., making reference to places, dates or events), labels provide a midlevel description of the image (i.e., in terms of the objects that can be seen in the image). Accordingly, the main assumption of this work is that by combining information from text and labels we can develop very effective retrieval methods. We study standard information fusion techniques for combining both sources of information. However, whereas the performance of such techniques is highly competitive, they cannot capture effectively the content of images. Therefore, we propose two novel representations for multimodal images that attempt to exploit the semantic cohesion among terms from different modalities. Such representations are based on distributional term representations widely used in computational linguistics. Under the considered representations the content of an image is modeled by a distribution of cooccurrences over terms or of occurrences over other images, in such a way that the representation can be considered an expansion of the multimodal terms in the image. We report experimental results using the SAIAPR TC12 benchmark on two sets of topics used in ImageCLEF competitions with manually and automatically generated labels. Experimental results show that the proposed representations outperform significantly both, standard multimodal techniques and unimodal methods. Results on manually assigned labels provide an upper bound in the retrieval performance that can be obtained, whereas results with automatically generated labels are encouraging. The novel representations are able to capture more effectively the content of multimodal images. We emphasize that although we have applied our representations to multimedia image retrieval the same formulation can be adopted for modeling other multimodal documents (e.g., videos).
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By
BeltránMárquez, Jessica; Chávez, Edgar; Favela, Jesús
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2 Citations
Automatic identification of activities can be used to provide information to caregivers of persons with dementia for identifying assistance needs. Environmental audio provides significant and representative information of the context, making microphones a choice to identify activities automatically. However, in real situations, the audio captured by microphones comes from overlapping sound sources, making its identification a challenge for audio analysis and retrieval. In this paper we propose a succinct representation of the signal by measuring the multiband spectral entropy of the signal frame by frame, followed by a cosine transform and binary codification, we call this the Cosine MultiBand Spectral Entropy Signature (CMBSES). To test our proposal, we created a database of a mixup of triples from a collection of nine environmental sounds in four different signaltonoise ratios (SNR). We codified both the original sounds and the triples and then searched all the original sounds in the mixup collection. To establish a ground truth we also tested the same database with 48 people of assorted ages. Our feature extraction outperforms the stateoftheart Mel Frequency Cepstral Coefficients (MFCC) and it also surpass humans in the experiment.
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By
Montero, Elizabeth; Riff, MaríaCristina; PérezCaceres, Leslie; Coello Coello, Carlos A.
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2 Citations
Currently, there exist several offline calibration techniques that can be used to finetune the parameters of a metaheuristic. Such techniques require, however, to perform a considerable number of independent runs of the metaheuristic in order to obtain meaningful information. Here, we are interested on the use of this information for assisting the algorithm designer to discard components of a metaheuristic (e.g., an evolutionary operator) that do not contribute to improving its performance (we call them “ineffective components”). In our study, we experimentally analyze the information obtained from three offline calibration techniques: FRace, ParamILS and Revac. Our preliminary results indicate that these three calibration techniques provide different types of information, which makes it necessary to conduct a more indepth analysis of the data obtained, in order to detect the ineffective components that are of our interest.
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By
Sakai, Toshinori; Urrutia, Jorge
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Let S = {s(1), …, s(n) } be a permutation of the integers {1,…, n}. A subsequence of S with elements {s(i_{1}), …, s(i_{k})} is called an increasing subsequence if s(i_{1}) < ⋯ < s(i_{k}); It is called a decreasing subsequence if s(i_{1}) > ⋯ > s(i_{k}). The weight of a subsequence of S, is the sum of its elements. In this paper, we prove that any permutation of {1, …, n} contains an increasing or a decreasing subsequence of weight greater than
$n\sqrt{2n/3}$
.
Our motivation to study the previous problem arises from the following problem: Let P be a set of n points on the plane in general position, labeled with the integers {1, …,n} in such a way that the labels of different points are different. A noncrossing path Π with vertices in P is an increasing path if when we travel along it, starting at one of its endpoints, the labels of its vertices always increase. The weight of an increasing path, is the sum of the labels of its vertices. Determining lower bounds on the weight of the heaviest increasing path a point set always has.
We also study the problem of finding a noncrossing matching of the elements of P of maximum weight, where the weight of an edge with endpoints i, j ∈ P is min{i,j}.
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By
DíazBáñez, José M.; FabilaMonroy, Ruy; PérezLantero, Pablo
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Let n be an even natural number and let S be a set of nred and nblue points in general position in the plane. Let p ∉ S be a point such that S ∪ {p} is in general position. A radial ordering of S with respect to p is a circular ordering of the elements of S by angle around p. A colored radial ordering is a radial ordering of S in which only the colors of the points are considered. We show that: the number of distinct radial orderings of S is at most O(n^{4}) and at least Ω(n^{2}); the number of colored radial orderings of S is at most O(n^{4}) and at least Ω(n); there exists sets of points with Θ(n^{4}) colored radial orderings and sets of points with only O(n^{2}) colored radial orderings.
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Berkhoff, Christian; Ochoa, Sergio F.; Pino, José A.; Favela, Jesús; Oliveira, Jonice; Guerrero, Luis A.
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One of the keystones of vision of AmI is the ability of multiple devices to seamlessly communicate within themselves. To this end, this article presents a mobile computer application which is able to smoothly connect via WiFi to largescreen TVs and mobile computer devices. Thus the application allows implementing shared displays with minimal effort and cost. These shared displays have shown to be useful to support meetings and informal encounters in various scenarios, such as at home, hospitals or business settings. The application was tested in real scenarios with encouraging results.
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Sato, Hiroyuki; Coello Coello, Carlos A.; Aguirre, Hernán E.; Tanaka, Kiyoshi
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1 Citations
To realize effective genetic operation in evolutionary manyobjective optimization, crossover controlling the number of crossed genes (CCG) has been proposed. CCG controls the number of crossed genes by using an userdefined parameter α. CCG with small α significantly improves the search performance of multiobjective evolutionary algorithm in manyobjective optimization by keeping small the number of crossed genes. However, to achieve high search performance by using CCG, we have to find out an appropriate parameter α by conducting many experiments. To avoid parameter tuning and automatically find out an appropriate α in a single run of the algorithm, in this work we propose an adaptive CCG which adopts the parameter α during the solutions search. Simulation results show that the values of α controlled by the proposed method converges to an appropriate value even when the adaptation is started from any initial values. Also we show the adaptive CCG achieves more than 80% with a single run of the algorithm for the maximum search performance of the static CCG using an optimal α^{*}.
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GarcíaMireles, Gabriel Alberto; Moraga, Ma Ángeles; García, Félix; Piattini, Mario
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2 Citations
Software organizations are currently required to implement more than one software process improvement model concurrently. Several multimodel initiatives have appeared to support this situation, and existing proposals address integration from the process perspective, considering models such as CMMI, ISO 90003, ISO/IEC 12207, and ISO/IEC 15504. These efforts attempt to understand how to integrate process focused models in order to optimize resources and obtain the expected benefits. However, as the eventual aim of process improvement is to improve software product quality, it is also important to consider product quality models in harmonization efforts. In this paper, the result of mapping models based on both, (process and product) quality perspectives, is presented. The method used is also briefly described and applied to map ISO/IEC 25010 onto CMMIDEV and ISO/IEC 12207. The result shows that process oriented improvement models consider product quality characteristics during the early stages of the software development life cycle, and that process improvement initiatives can therefore be driven by product quality improvement goals.
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Hernández, Daniel; Olague, Gustavo; Clemente, Eddie; Dozal, León
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1 Citations
Active, animate, purposive or behavioral vision are all understood as a research area where a seeing system interacts with the world in such a way of creating a balance between perception and action. In particular, it is said that a selective perception process in combination with a specific motionaction works as a unique complex system that accomplishes a visuomotor task. In the present work, this is understood as a visual behavior. This work describes a realworking system composed of a camera mounted on a robotic manipulator that is used as a research platform for evolving a visual routine specially designed in the estimation of specific motionactions. The idea is to evolve an interest point detector with the goal of simplifying a wellknown simultaneous localization and map building system. Experimental results shows as a proofofconcept that the proposed system is able to design a specific interest point detector for the case of a straightline displacement with the advantage of eliminating a number of heuristics.
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Cano, Javier; Tóth, Csaba D.; Urrutia, Jorge
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For every n ∈ ℕ, there is a straightline drawing D_{n} of a planar graph on n vertices such that in any crossingfree straightline drawing of the graph, at most O(n^{.4982}) vertices lie at the same position as in D_{n}. This improves on an earlier bound of
$O(\sqrt{n})$
by Goaoc et al. [6].
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OmañaPulido, Elsa; RiveraCampo, Eduardo
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The twisted graph T_{n} is a complete topological graph with n vertices v_{1},v_{2},…,v_{n} in which two edges v_{i}v_{j} (i < j) and v_{s}v_{t} (s < t) cross each other if and only if i < s < t < j or s < i < j < t. We study several properties concerning plane topological subgraphs of T_{n}.
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Vega, Gerardo; Vázquez, Carlos A.
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3 Citations
A remarkably general result which provides the evaluation of a family of exponential sums was presented by Marko J. Moisio (Acta Arithmetica, 93 (2000) 117119). In this work, we use a particular instance of this general result in order to determine the value distribution of a particular exponential sum. Then, motivated by some new and fresh original ideas of Changli Ma, Liwei Zeng, Yang Liu, Dengguo Feng and Cunsheng Ding (IEEE Trans. Inf. Theory, 571 (2011) 397402), we use this value distribution in order to obtain the weight distribution of a family of reducible cyclic codes. As we will see later, all the codes in this family are nonprojective cyclic codes. Furthermore, they can be identified in a very easy way. In fact, as a byproduct of this easy identification, we will be able to determine the exact number of cyclic codes in a family when length and dimension are given.
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Hernández, Apolo H.; RománAlonso, Graciela; CastroGarcía, Miguel A.; AguilarCornejo, Manuel; DomínguezDomínguez, Santiago; BuenabadChávez, Jorge
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The Data List Management Library (DLML) processes data lists in parallel, balancing the workload transparently to programmers. Programmers only need to organise data into a list, use DLML functions to insert and get data items, and specify the sequential function(s) to process each data item according to the application logic. The first design of DLML was targeted for use at a single cluster.
This paper presents DLMLGrid, a software architecture for DLML to run in Grid environments composed of multiple distributed clusters. The architecture is hierarchical and tends to localise communication within clusters, thus reducing communication overhead. Using OpenVPN, we implemented a prototype version of DLMLGrid to gather empirical results on its performance using two clusters and two applications whose workload is static and dynamically generated. DLMLGrid performs much better than DLML overall.
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AlvaradoCervantes, Rodolfo; FelipeRiveron, Edgardo M.
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1 Citations
We present an interactive, semiautomatic image segmentation method that processes the color information of each pixel as a unit, thus avoiding color information scattering. The color information of every pixel is integrated in the segmented image by an adaptive color similarity function designed for direct color comparisons. The border between the achromatic and chromatic zones in the HSI color model has been transformed in order to improve the quality of the pixels segmentation when their colors are very obscure and very clear. The color integrating technique is direct, simple and computationally inexpensive, and it has also good performance in low chromaticity and low contrast images. It is shown that segmentation accuracy is above 95% as average and that the method is fast. These results are significant when compared to other solutions found in the current literature.
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Vilariño, Darnes; Pinto, David; Beltrán, Beatriz; León, Saul; Castillo, Esteban; Tovar, Mireya
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Normalization of SMS is a very important task that must be addressed by the computational community because of the tremendous growth of services based on mobile devices, which make use of this kind of messages. There exist many limitations on the automatic treatment of SMS texts derived from the particular writing style used. Even if there are suficient problems dealing with this kind of texts, we are also interested in some tasks requiring to understand the meaning of documents in different languages, therefore, increasing the complexity of such tasks. Our approach proposes to normalize SMS texts employing machine translation techniques. For this purpose, we use a statistical bilingual dictionary calculated on the basis of the IBM4 model for determining the best translation for a given SMS term. We have compared the presented approach with a traditional probabilistic method of information retrieval, observing that the normalization model proposed here highly improves the performance of the probabilistic one.
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Urrutia, Jorge
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In 1973, Victor Klee posed the following question: How many guards are necessary, and how many are sufficient to patrol the paintings and works of art in an art gallery with n walls?
This wonderfully naïve question of combinatorial geometry has, since its formulation, stimulated a plethora of fun papers, surveys and a book, most of them written in the last twentyfive years. Several variations of the original Art Gallery Problem have appeared, and continue to appear in the literature. In this talk, we will present some recent work motivated by the following problem.
Experience dictates that while trying to locate the best location for a wireless within a building, the main factor that attenuates the signal of a wireless modem, is the number of walls that a signal has to cross. Thus we call a wireless modem (from now on a modem) a kmodem if the signal it transmits is capable of crossing kwalls of a building, and still provide a strong enough signal.
A generalization of Klee’s question is thus: How many kmodems are necessary, and how many are sufficient to cover the interior of an art gallery with nwalls? For k = 0, our problem reduces to the original Art Gallery Problem.
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Llarena, Adalberto; Rosenblueth, David A.
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Model checking can be a very useful set of tools in the field of decisionmaking when evaluating global playing strategies. This paper analyzes the playing strategies of a group of humanoid robots equipped with an onboard camera, assuming they have a probabilistic playing model. Once a playing model is proposed as a global strategy for deciding between attacking and defending, this model is analyzed in order to modify the values of its parameters depending on the robot capabilities of both teams.
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NavaDíaz, Salvador W.; Chavira, Gabriel; Rolón, Julio C.; Orozco, Jorge
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1 Citations
This article uses the vision of Ambient Intelligence that proposes distributing various computing devices that react to the presence of users. As a first approximation, we have thus modeled the context by identifying users. To do this, we rely on the model of the five W’s. Similarly, this article presents a way to generate contextawareness, supported in the MARCado framework. It also presents a mechanism that allows behavior of contextual information to obtain contextaware services. These elements have been implemented after adapting sensorial technologies, which provide simple input into the system.
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AguilarGonzález, Pablo M.; Kober, Vitaly
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Correlation filters for object detection use information about the appearance and shape of the object of interest. Therefore, detection performance degrades when the appearance of the object in the scene differs from the appearance used in the filter design process. This problem has been approached by utilizing composite filters designed from a training set containing known views of the object of interest. However, common composite filter design is usually carried out under the assumption that the ideal appearance and shape of the target are known. In this work we propose an algorithm for composite filter design using noisy training images. The algorithm is a modification of the class synthetic discriminant function technique that uses arbitrary filter impulse responses. Furthermore, filters can be adapted to achieve a prescribed discrimination capability for a class of backgrounds if a representative sample is known. Computer simulation results obtained with the proposed algorithm are presented and compared with those of common composite correlation filters.
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RamírezAcosta, Alejandro A.; GarcíaVázquez, Mireya S.; Nakano, Mariko
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The transmission over errorprone networks of still images or videos coded by blockbased techniques like JPEG and MPEG respectively, may lead to block loss degrading, particularly the visual quality of images. Working under this environment, such as wireless communication where retransmission may be not feasible, application of error concealment techniques is consequently required to reduce degradation caused by the missing information. This paper surveys algorithms for spatial error concealment and proposes an adaptive and effective method based on edge analysis that performs well in current situations where significant loss of information is present and the data of the past reference images are not also available. The proposed method and the reviewed algorithms were implemented, tested and compared. Experimental results show that the proposed approach outperforms existing methods by up to 8.6 dB on average.
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Pérez Maldonado, Yara; Caballero Morales, Santiago Omar; Cruz Ortega, Roberto Omar
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1 Citations
Hidden Markov Models (HMMs) have been widely used for Automatic Speech Recognition (ASR). Iterative algorithms such as Forward  Backward or BaumWelch are commonly used to locally optimize HMM parameters (i.e., observation and transition probabilities). However, finding more suitable transition probabilities for the HMMs, which may be phonemedependent, may be achievable with other techniques. In this paper we study the application of two Genetic Algorithms (GA) to accomplish this task, obtaining statistically significant improvements on unadapted and adapted Speaker Independent HMMs when tested with different users.
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LópezMonroy, Adrián Pastor; MontesyGómez, Manuel; VillaseñorPineda, Luis; CarrascoOchoa, Jesús Ariel; MartínezTrinidad, José Fco.
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This paper proposes a novel representation for Authorship Attribution (AA), based on Concise Semantic Analysis (CSA), which has been successfully used in Text Categorization (TC). Our approach for AA, called Document Author Representation (DAR), builds document vectors in a space of authors, calculating the relationship between textual features and authors. In order to evaluate our approach, we compare the proposed representation with conventional approaches and previous works using the c50 corpus. We found that DAR can be very useful in AA tasks, because it provides good performance on imbalanced data, getting comparable or better accuracy results.
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KuriMorales, Angel; LopezPeña, Ignacio
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A method to determine the position of a mobile robot using machine learning strategies was introduced in [1]. The method raises the possibility to decrease the size of database that holds the images that describe an area where a robot will localize itself. The present work does a statistical validation of the approach by calculating the Hamming and Euclidean distances between all the images using on the one hand all their pixels and on the other hand the reduced set of pixels obtained by the GA as described in [1]. To perform the analysis, a new series of images were taken from a specific position at several angles in both horizontal (pan) and vertical (tilt). These images were compared using two different measures: a) the Hamming distance and b) the Euclidean distance to determine how similar are one from another.
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MoyaSánchez, E. Ulises; BayroCorrochano, Eduardo
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Atomic Functions are widely used in different applications in image processing, pattern recognition, computational physics and also in the digital interpretation of signal measurements. In 1D signals, is usual to compute the phase and the magnitude of a signal using the analytic signal (the signal and its Hilbert transform using complex numbers). However, for high dimensional signals the monogenic signal (the signal and its Riesz transform) has been used to obtain the local phase and orientation with good results. The main aim of this work is to present a new way to make the computation of the Hilbert transform using the atomic function. The computation of the Hilbert transform take relevance when the phase computation is required.
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Alejo, R.; Toribio, P.; Valdovinos, R. M.; PachecoSanchez, J. H.
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In this paper we propose a modified backpropagation to deal with severe twoclass imbalance problems. The method consists in automatically to find the oversampling rate to train a neural network (NN), i.e., identify the appropriate number of minority samples to train the NN during the learning stage, so to reduce training time. The experimental results show that the performance proposed method is a very competitive when it is compared with conventional SMOTE, and its training time is lesser.
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SilvánCárdenas, José Luis
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A watershed segmentation algorithm is proposed for automatic extraction of tree crowns from LiDAR data to support 3d modelling of forest stands. A relatively sparse LiDAR point cloud was converted to a surface elevation in raster format and a canopy height model (CHM) extracted. Then, the segmentation method was applied on the CHM and results combined with the original point cloud to generate models of individual tree crowns. The method was tested in 200 circular plots (400 m^{2}) located over 50 sites of a conservation area in Mexico City. The segmentation method exhibited a moderate to perfect detection rate on 66% of plots tested. One major factor for a poor detection was identified as the relatively low sampling rate of LiDAR data with respect to crown sizes.
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Figueroa Mora, Karina; Paredes, Rodrigo
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Proximity searching consists in retrieving the most similar objects to a given query. This kind of searching is a basic tool in many fields of artificial intelligence, because it can be used as a search engine to solve problems like
$kN\!N$
searching. A common technique to solve proximity queries is to use an index. In this paper, we show a variant of the permutation based index, which, in his original version, has a great predicting power about which are the objects worth to compare with the query (avoiding the exhaustive comparison). We have noted that when two permutants are close, they can produce small differences in the order in which objects are revised, which could be responsible of finding the true answer or missing it. In this paper we pretend to mitigate this effect. As a matter of fact, our technique allows us both to reduce the index size and to improve the query cost up to 30%.
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Rios Soria, David J.; Schaeffer, Satu Elisa
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We present a software tool created for humancomputer interaction based on hand gestures. The underlying algorithm utilizes computer vision techniques. The tool is able to recognize in realtime six different hand signals, captured using a web cam. Experiments conducted to evaluate the system performance are reported.
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HernándezRodríguez, Felipe; Castelán, Mario
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Photometric sampling is a process where the surface normals of an object are estimated through the excitation of the object’s surface and a rotating light source around it. The method can be regarded as a special case of photometric stereo when extensive sampling is performed in order to calculate surface normals. The classic photometric sampling approach considers only variations around the azimuth angle of the moving light source. As a consequence, additional attention has to be be paid to the recovery of the light source directions and the removal of specular and shadowed regions. This paper investigates the effect of including variations around the zenith angle of the light source vector in a photometric sampling framework, developing a geometric approach to estimate the surface normal vectors. Experiments show that increasing the number of samples along the zenith variation benefits the estimation of the surface normals.
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YaurimaBasaldua, Victor Hugo; Tchernykh, Andrei; CastroGarcia, Yair; VillagomezRamos, Victor Manuel; Burtseva, Larisa
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This paper addresses nonpreemptive offline scheduling parallel jobs on a Grid. We consider a Grid scheduling model with two stages. At the first stage, jobs are allocated to a suitable Grid site, while at the second stage, local scheduling is independently applied to each site. In this environment, one of the big challenges is to provide a job allocation that allows more efficient use of resources and user satisfaction. In general, the criteria that help achieve these goals are often in conflict. To solve this problem, twoobjective genetic algorithm is proposed. We conduct comparative analysis of five crossover and three mutation operators, and determine most influential parameters and operators. To this end multi factorial analysis of variance is applied.
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Navarrete, Dulce J.; Morales, Eduardo F.; Sucar, Luis Enrique
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Object recognition from images is traditionally based on a large training set of previously annotated images which is impractical for some applications. Also, most methods use only local or global features. Due to the nature of objects some features are better suited for some objects, so researchers have recently combined both types of features to improve the recognition performance. This approach, however, is not sufficient for the recognition of generic objects which can take a wide variety of appearances. In this paper, we propose a novel object recognition system that: (i) uses a small set of images obtained from the Web, (ii) induces a set of models for each object to deal with polymorphism, and (iii) optimizes the contribution of local and global features to deal with different types of objects. We performed tests with both generic and specific objects, and compared the proposed approach against base classifiers and stateoftheart systems with very promising results.
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BonillaHuerta, Edmundo; Duval, Béatrice; Hernández, José C. Hernández; Hao, JinKao; MoralesCaporal, Roberto
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3 Citations
The microarray data classification problem is a recent complex pattern recognition problem. The most important goal in supervised classification of microarray data, is to select a small number of relevant genes from the initial data in order to obtain high predictive classification accuracy. With the framework of a hybrid filterwrapper, we study in this paper the role of the multiparent recombination operator. For this purpose, we introduce a Random Multi Parent crossover (RMPX) and we analyze their effects in a genetic algorithm (GA) which is combined with Fisher’s Linear Discriminant Analysis (LDA). This hybrid algorithm has the major characteristic that the GA uses not only a LDA classifier in its fitness function, but also LDA’s discriminant coefficients to integrate a multiparent specialized crossover and mutation operation to improve the performance of gene selection. In the experimental results it is observed that RPMX operator work very well by achieving lower classification error rates.
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Sánchez, Ma. Guadalupe; Vidal, Vicente; Bataller, Jordi
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In this paper, we report a study on the parallelization of an algorithm for removing impulsive noise in images. The algorithm is based on the concept of peer group and fuzzy metric. We have developed implementations using Open MultiProcessing (OpenMP) and Compute Unified Device Architecture (CUDA) for Graphics Processing Unit (GPU). Many sequential algorithms have been proposed to remove noise, but their computational cost is excessive for realtime processing of large images. We developed implementations for a multicore CPU, for a multiGPU (several GPUs) and for a combination of both. These implementations were compared also with different sizes of the image in order to find out the settings with the best performance. A study is made using the shared memory and texture memory to minimize access time to data in GPU global memory. The result shows that when the image is distributed in multicore and multiGPU a greater number of Mpixels/second are processed.
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Castillo, Carlos; Valero, Héctor; Ramos, José Guadalupe; Silva, Josep
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3 Citations
Retrieving information from Internet is a difficult task as it is demonstrated by the lack of realtime tools able to extract information from webpages. The main cause is that most webpages in Internet are implemented using plain (X)HTML which is a language that lacks structured semantic information. For this reason much of the efforts in this area have been directed to the development of techniques for URLs extraction. This field has produced good results implemented by modern search engines. But, contrarily, extracting information from a single webpage has produced poor results or very limited tools. In this work we define a novel technique for information extraction from single webpages or collections of interconnected webpages. This technique is based on DOM distances to retrieve information. This allows the technique to work with any webpage and, thus, to retrieve information online. Our implementation and experiments demonstrate the usefulness of the technique.
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Vilariño Ayala, Darnes; Pinto, David; Silverio, Saúl León; Castillo, Esteban; Vidal, Mireya Tovar
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A recursive approach for keyword search on XML data for the AdHoc Search Task of INEX 2011 is presented in this paper. The aim of this approach was to detect the concrete part (in the representation tree) of the XML document containing the expected answer. For this purpose, we initially obtain a tree structure, which represents an XML document, tagged by levels. A typical search engine based on posting lists is used in order to determine those documents that match in some degree with the terms appearing in the given query(topic). Thereafter, in a recursively process, we navigate into the tree structure until we find the best match for the topic. The obtained results are shown and compared with the best overall submission score obtained in the competition.
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BarrónEstrada, M. L.; ZatarainCabada, Ramón; Beltrán V., J. A.; Cibrian R., F. L.; Pérez, Yasmín Hernández
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2 Citations
In this paper we present an intelligent and affective tutoring system designed and implemented within a social network. The tutoring system evaluates cognitive and affective aspects and applies fuzzy logic to calculate the exercises that are presented to the student. We are using Kohonen neural networks to recognize emotions through faces and voices and multiattribute utility theory to encourage positive affective states. The social network and the intelligent tutoring system are integrated into a Web application. We present preliminary results with different groups of students using this software tool.
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AndalonGarcia, Irma R.; Chavoya, Arturo; MedaCampaña, M. E.
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The search of a multiple sequence alignment (MSA) is a wellknown problem in bioinformatics that consists in finding a sequence alignment of three or more biological sequences. In this paper, we propose a parallel iterative algorithm for the global alignment of multiple biological sequences. In this algorithm, a number of processes work independently at the same time searching for the best MSA of a set of sequences. It uses a Longest Common Subsequence (LCS) technique in order to generate a first MSA. An iterative process improves the MSA by applying a number of operators that have been implemented to produce more accurate alignments. Simulations were made using sequences from the UniProKB protein database. A preliminary performance analysis and comparison with several common methods for MSA shows promising results. The implementation was developed on a cluster platform through the use of the standard Message Passing Interface (MPI) library.
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CastilloMuñiz, Efrain; RiveraRovelo, Jorge; BayroCorrochano, Eduardo
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The use of haptic interfaces in surgery could provide the surgeon useful sensing information about the patient tissues. Our goal in this work, is to use the haptic interface to obtain some sample points on the surface of an object or organ tissue in medical applications. This elasticity information feeds an artificial neural network. The output of the neural network is an approximation of the compliance of the object which is touched, as well as the coordinates of 3D surface points which in fact are used for the 3D surface reconstruction of the object. Experimental results show that the reconstruction of objects from a elasticity point of view is possible, and that the use of a haptic interface can improve the performance of 3D reconstruction algorithms.
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CruzPerez, Claudia; Starostenko, Oleg; UcedaPonga, Fernando; AlarconAquino, Vicente; ReyesCabrera, Leobardo
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5 Citations
In this paper we present a novel approach for automatic segmentation and recognition of reCAPTCHA in Web sites. It is based on CAPTCHA image preprocessing with character alignment, morphological segmentation with threecolor bar character encoding and heuristic recognition. The original proposal consists in exploiting threecolor bar code for characters in CAPTCHA for their robust segmentation with presence of random collapse overlapping letters and distortions by particular patterns of waving rotation. Additionally, a novel implementation of SVMbased learning classifier for recognition of combinations of characters in training corpus has been proposed that permits to increment more than twice the recognition success rate without time extension of system response. The main goal of this research is to reduce vulnerability of CAPTCHA from spam and frauds as well as to provide a novel approach for recognizing either handwritten or degraded and damaged texts in ancient manuscripts. Our designed framework implementing the proposed approach has been tested in realtime applications with sites used CAPTCHAS achieving segmentation success rate about of 82% and recognition success rate about of 94%.
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GaliciaHaro, Sofía N.; Gelbukh, Alexander
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1 Citations
This paper reports research on temporal expressions. The analyzed phrases include a common temporal expression for a period of years reinforced by an adverb of time. Some of these phrases are agerelated expressions. We analyzed samples of this type obtained from the Internet for Spanish and Italian to determine appropriate annotations for marking up text and possible translations. We present the results of comparison for four selected classes.
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Tellez, Eric Sadit; Chavez, Edgar; Figueroa, Karina
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2 Citations
Some metric indexes, like the pivot based family, can natively trade space for query time. Other indexes may have a small memory footprint and still outperform the pivot based approach; but are unable to increase the memory usage to boost the query time. In this paper we propose a new metric indexing technique with an algorithmic mechanism to lift the performance of otherwise rigid metric indexes.
We selected the well known List of Clusters (LC) as the base data structure, obtaining an index which is orders of magnitude faster to build, with memory usage adaptable to the intrinsic dimension of the data, and faster at query time than the original LC. We also present a nearest neighbor algorithm, of independent interest, which is optimal in the sense that requires the same number of distance computations as a range query with the radius of the nearest neighbor.
We present exhaustive experimental evidence supporting our claims, for both synthetic and real world datasets.
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Aranda, Ramón; Rivera, Mariano; RamírezManzanares, Alonso
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We present a new regularization approach for Diffusion Basis Functions fitting to estimate in vivo brain the axonal orientation from Diffusion Weighted Magnetic Resonance Images. That method assumes that the observed Magnetic Resonance signal at each voxel is a linear combination of a given diffusion basis functions; the aim of the approach is the estimation of the coefficients of the linear combination. An issue with the Diffusion Basis Functions method is the overestimation on the number of tensors (associated with different axon fibers) within a voxel due to noise, namely, the over fitting of the noisy signal. Our proposal overcomes such an overestimation problem. In additionally, we propose a metric to compare the performance of multifiber estimation algorithms. The metric is based on the Earth Mover’s Distance and allows us to compare in a single metric the orientation, size compartment and the number of axon bundles between two different estimations. The improvements of our two proposals is shown on synthetic and real experiments.
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Madrid, Humberto; Guerra, Valia; Rojas, Marielba
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Randomized algorithms for processing massive data sets have shown to be a promising alternative to deterministic techniques. Sampling strategies are an essential aspect of randomized algorithms for matrix computations. In this work, we show that strategies that are effective or even optimal in the general case, can fail when applied to illconditioned matrices. Our experimental study suggests that there exists a relationship between sampling performance and conditioning of the matrices involved. We present an explanation for this behavior and propose a novel, efficient, and accurate sampling strategy for randomized multiplication of affinity matrices in image segmentation.
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Schillaci, Guido; Lara, Bruno; Hafner, Verena V.
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8 Citations
In this paper, we present internal simulations as a methodology for human behaviour recognition and understanding. The internal simulations consist of pairs of inverse forward models representing sensorimotor actions. The main advantage of this method is that it both serves for action selection and prediction as well as recognition. We present several humanrobot interaction experiments where the robot can recognize the behaviour of the human reaching for objects.
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HerreraNavarro, Ana Marcela; JiménezHernández, Hugo; TerolVillalobos, Iván Ramón
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The circle is a useful morphological structure: in many situations, the importance is focused on the measuring of the similarity of a perfect circle against the object of interest. Traditionally, the wellknown geometrical structures are employed as useful geometrical descriptors, but an adequate characterization and recognition are deeply affected by scenarios and physical limitations (such as resolution and noise acquisition, among others). Hence, this work proposes a new circularity measure which offers several advantages: it is not affected by the overlapping, incompleteness of borders, invariance of the resolution, or accuracy of the border detection. The propounded approach deals with the problem as a stochastic nonparametric task; the maximization of the likelihood of the evidence is used to discover the true border of the data that represent the circle. In order to validate the effectiveness of our proposal, we compared it with two recently effective measures: the mean roundness and the radius ratio.
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ChavezAlvarez, Rocio; Chavoya, Arturo; LopezMartin, Cuauhtemoc
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After the sequencing of whole genomes and the identification of the genes contained in them, one of the main challenges remaining is to understand the mechanisms that regulate the expression of genes within the genome in order to gain knowledge about structural, biochemical, physiological and behavioral characteristics of organisms. Some of these mechanisms are controlled by socalled Genetic Regulatory Networks (GRNs). Boolean networks can help model biological GRNs. In this paper, a genetic algorithm is used to make inferences in Boolean networks, in combination with the QuineMcCluskey algorithm, when not all the output states of the genes have been determined. This lack of information could be treated as “don’t care” states. Genetic algorithms are useful in multiobjective optimization problems, such as minimization of Gene Regulatory Functions, where it is important not only to have the smallest quantity of disjunctions, but also the smallest quantity of genes involved in the regulation.
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Clemente, Eddie; Olague, Gustavo; Dozal, León; Mancilla, Martín
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Computational neuroscience is a discipline devoted to the study of brain function from an information processing standpoint. The ventral stream, also known as the “what” pathway, is widely accepted as the model for processing the visual information related to object identification. This paper proposes to evolve a mathematical description of the ventral stream where key features are identified in order to simplify the whole information processing. The idea is to create an artificial ventral stream by evolving the structure through an evolutionary computing approach. In previous research, the “what” pathway is described as being composed of two main stages: the interest region detection and feature description. For both stages a set of operations were identified with the aim of simplifying the total computational cost by avoiding a number of costly operations that are normally executed in the template matching and bag of feature approaches. Therefore, instead of applying a set of previously learned patches, product of an offline training process, the idea is to enforce a functional approach. Experiments were carried out with a standard database and the results show that instead of 1200 operations, the new model needs about 200 operations.
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Du, Jingzhe; Kranakis, Evangelos; Ponce, Oscar Morales; Rajsbaum, Sergio
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Consider a network of n directional antennae in the plane. We consider the problem of efficient neighbor discovery in a (synchronous) network of sensors employing directional antennae. In this setting sensors send messages and listen for messages by directing their antennae towards a specific direction (which is not necessarily known in advance). In our model the directional antennae can be rotated by the sensors as required so as to discover all neighbors in their vicinity. In this paper we will limit ourselves to the (D,D) communication model whereby sensors employ directional antennae with identical transmission/reception beam widths. Our methodology is based on techniques for symmetry breaking so as to enable sender/receiver communication. We provide 1) deterministic algorithms that introduce delay in the rotation of the antennae and exploit knowledge of the existence of a vertex coloring of the network, and 2) randomized algorithms that require knowledge only of an upper bound on the size of the network so as to accomplish neighbor discovery. In both instances we study tradeoffs on the efficiency of the algorithms proposed.
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Baruch, Ieroham; Hernandez, Sergio M.; MorenoCruz, Jacob; Gortcheva, Elena
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A new Modular Recurrent Trainable Neural Network (MRTNN) has been used for system identification of twomassresortdamper nonlinear oscillatory plant. The first MRTNN module identified the exponential part of the unknown plant and the second one  the oscillatory part of the plant. The plant has been controlled by a direct adaptive neural control system with integral term. The RTNN controller used the estimated parameters and states to suppress the plant oscillations and the static plant output control error is reduced by an Iterm added to the control.
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Williams, Lance R.
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We show how expressions written in a functional programming language can be robustly evaluated on a modular asynchronous spatial computer by compiling them into a distributed virtual machine comprised of reified bytecodes undergoing diffusion and communicating via messages containing encapsulated virtual machine states. Because the semantics of the source language are purely functional, multiple instances of each reified bytecode and multiple execution threads can coexist without inconsistency in the same distributed heap.
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Aguilar, José Alfonso; Garrigós, Irene; Mazón, JoseNorberto; Zaldívar, Anibal
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Due to the dynamic nature of the Web as well as its heterogeneous audience, web applications are more likely to rapidly evolve leading to inconsistencies among requirements during the development process. With the purpose to deal with these inconsistencies, web developers need to know dependencies among requirements considering that the understanding of these dependencies helps in better managing and maintaining web applications. In this paper, an algorithm has been defined and implemented in order to analyze dependencies among functional and nonfunctional requirements (in a goaloriented approach) for understanding which is the impact derived from a change during the ModelDriven Web Engineering process. This Impact Analysis would support web developer in selecting requirements to be implemented ensuring that web applications finally satisfy the audience.
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DuarteVillaseñor, Miriam Mónica; CarrascoOchoa, Jesús Ariel; MartínezTrinidad, José Francisco; FloresGarrido, Marisol
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Multiclass problems, i.e., classification problems involving more than two classes, are a common scenario in supervised classification. An important approach to solve this type of problems consists in using binary classifiers repeated times; within this category we find nested dichotomies. However, most of the methods for building nested dichotomies use a random strategy, which does not guarantee finding a good one. In this work, we propose new nonrandom methods for building nested dichotomies, using the idea of reducing misclassification errors by separating in the higher levels those classes that are easier to separate; and, in the lower levels those classes that are more difficult to separate. In order to evaluate the performance of the proposed methods, we compare them against methods that randomly build nested dichotomies, using some datasets (with mixed data) taken from the UCI repository.
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GuevaraSouza, Mauricio; Vallejo, Edgar E.
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This paper shows how the addition of Wolbachia infection can improve evolutionary function optimization by preventing the system from sticking at local optima. Firstly a variant of genetic algorithms that allows the introduction of Wolbachia is described. Then an application of this system to the optimization of a collection of mutimodal functions is described. Finally, we show how the introduction of Wolbachia infection improves the procedure in terms of both fitness and the number of generations required to obtain the solutions.
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LaureanoCruces, Ana Lilia; RamírezRodríguez, Javier
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In this paper we use REG, a graphbased system to study a fundamental problem of Natural Language Processing: the automatic summarization of documents. The algorithm models a document as a graph, to obtain weighted sentences. We applied this approach to the INEX@QA 2011 task (questionanswering). We have extracted the title and some key or related words according to two people from the queries, in order to recover 50 documents from english wikipedia. Using this strategy, REG obtained good results with the automatic evaluation system FRESA.
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Barriga, Silviano Díaz; Rodríguez, LuisFelipe; Ramos, Félix; Ramos, Marco
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Computational models of cognitive processes are usually developed as separate components. The importance of the interactions among these models has been widely disregarded. In recent years, research on the brain information processing has focused on the interrelationships among cognitive functions, providing a wealth of evidence capable of informing the development of more integrative computational models of cognition. In this paper, we present a braininspired computational model of emotion and attention interaction. This model addresses some aspects of the interplay between these two processes and is developed to be included in cognitive architectures of intelligent agents. Simulations based on the dotprobe paradigm are carried out to validate the proposed model.
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RodriguezTello, Eduardo; Betancourt, Luis Carlos
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This paper presents an Improved Memetic Algorithm (IMA) designed to compute nearoptimal solutions for the antibandwidth problem. It incorporates two distinguishing features: an efficient heuristic to generate a good quality initial population and a local search operator based on a Stochastic Hill Climbing algorithm. The most suitable combination of parameter values for IMA is determined by employing a tunning methodology based on Combinatorial Interaction Testing. The performance of the finetunned IMA algorithm is investigated through extensive experimentation over well known benchmarks and compared with an existing stateoftheart Memetic Algorithm, showing that IMA consistently improves the previous bestknown results.
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Ita, Guillermo; Altamirano, Luis; LópezLópez, Aurelio; Moyao, Yolanda
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We present a novel method to study the tendencies of vote in sectorial democratic elections. Our method is intended to determine the relevant profiles characterizing the political behavior of voters. Those profiles allow us to model how the voters, in a specific election organized by sectors, make their vote decision. Furthermore, the same set of profiles are used for representing the different strategies applied by the candidates that compete in the election.
We apply congestion games theory to simulate the distribution of the votes among the candidates, describing an automated way to estimate the likely number of votes for each candidate. Therefore, we can determine who will be the winner candidate of the election, according to a specific political scenario. We report the application of our model to simulate the elections of a director in a university setting, obtaining estimations very close to the actual outcomes.
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Dozal, León; Olague, Gustavo; Clemente, Eddie; Sánchez, Marco
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Brain informatics (BI) is a field of interdisciplinary study covering topics such as attention, memory, language, computation, learning and creativity, just to say a few. The BI is responsible for studying the mechanisms of human information processing. The dorsal stream, or “where”stream, is intimately related to the processing of visual attention. This paper proposes to evolve VAPs that learn to attend a given object within a scene. Visual attention is usually divided in two stages: feature acquisition and feature integration. In both phases there are specialized operators in the acquisition of a specific feature, called EVOs, and on the fusion of these features, called EFI. In previous research, those referred operators were established without considering the goal to be achieved. Instead of using established operators the idea is to learn and optimize them for the visual attention task. During the experiments we used a standard database of images for visual attention. The results provided in this paper show that our approach achieves impressive performance in the problem of focus visual attention over complex objects in challenging real world images on the first try.
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Madrigal, Francisco; Rivera, Mariano; Hayet, JeanBernard
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This paper describes an original strategy for using a datadriven probabilistic motion model into particle filterbased target tracking on video streams. Such a model is based on the local motion observed by the camera during a learning phase. Given that the initial, empirical distribution may be incomplete and noisy, we regularize it in a second phase. The hybrid discretecontinuous probabilistic motion model learned this way is then used as a sampling distribution in a particle filter framework for target tracking. We present promising results for this approach in some common datasets used as benchmarks for visual surveillance tracking algorithms.
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Navarro, René F.; Rodríguez, Marcela D.; Favela, Jesús
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We present an approach for personalizing nonpharmacological interventions for people with dementia (PwD) using ontologies. A successful intervention requires an individualized approach that considers the evolving needs of the PwD and the the caregiver. Personcentred models of dementia care focus on addressing individual needs and emotional reactions. From an in situ intervention with persons suffering with Alzheimer’s, we derived an ontological model to allow an Ambient Augmented Memory System (AAMS) to personalize the planning and execution of interventions to address problematic behaviors. Through an application scenario, we illustrate the flexibility of the ontology to provide the AAMS system with contextual information to personalize an intervention to assist PwD who have problems recalling their medication.
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Hernández Santiago, José; Cervantes, Jair; LópezChau, Asdrúbal; Lamont, Farid García
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In pattern recognition and data mining a data set is named skewed or imbalanced if it contains a large number of objects of certain type and a very small number of objects of the opposite type. The imbalance in data sets represents a challenging problem for most classification methods, this is because the generalization power achieved for classic classifiers is not good for skewed data sets. Many real data sets are imbalanced, so the development of new methods to face this problem is necessary. The SVM classifier has an exceptional performance for data sets that are not skewed, however for imbalanced sets the optimal separating hyper plane is not enough to achieve acceptable results. In this paper a novel method that improves the performance of SVM for skewed data sets is presented. The proposed method works by exciting the support vectors and displacing the separating hyper plane towards majority class. According to the results obtained in experiments with different skewed data sets, the method enhances not only the accuracy but also the sensitivity of SVM classifier on this kind of data sets.
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HernándezDomínguez, Jorge Sebastian; ToscanoPulido, Gregorio; Coello Coello, Carlos A.
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Particle swarm optimization (PSO) and differential evolution (DE) are metaheuristics which have been found to be very successful in a wide variety of optimization tasks. The high convergence rate of PSO and the exploratory capabilities of DE make them highly viable candidates to be used for solving multiobjective optimization problems (MOPs). In previous studies that we have undertaken [2], we have observed that PSO has the ability to launch particles in the direction of a leader (i.e., a nondominated solution) with a high selection pressure. However, this high selection pressure tends to move the swarm rapidly towards local optima. DE, on the other hand, seems to move solutions at smaller steps, yielding solutions close to their parents while exploring the search space at the same time. In this paper, we present a multiobjective particle swarm optimizer enhanced with a differential evolution scheme which aims to maintain diversity in the swarm while moving at a relatively fast rate. The goal is to avoid premature convergence without sacrificing much the convergence rate of the algorithm. In order to design our hybrid approach, we performed a series of experiments using the ZDT test suite. In the final part of the paper, our proposed approach is compared (using 2000, 3500, and 5000 objective function evaluations) with respect to four stateoftheart multiobjective evolutionary algorithms, obtaining very competitive results.
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Machuca Bautista, Berenice; Padilla Medina, José Alfredo; Sánchez Marín, Francisco Javier
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The electronic prototype for vision developed in this paper intends to show that it is possible to build an inexpensive and functional device which serves to partly compensate the sense of sight for visually impaired individuals through sensory substitution, by replacing some functions the sense of sight with functions of the sense of touch, with the proposed prototype, blind users receive electrical signals in the tips of their fingers generated from the capture of images objects with a camera and processed on a laptop to extract visual information.
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