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Beuchat, JeanLuc; GonzálezDíaz, Jorge E.; Mitsunari, Shigeo; Okamoto, Eiji; RodríguezHenríquez, Francisco; Teruya, Tadanori
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44 Citations
This paper describes the design of a fast software library for the computation of the optimal ate pairing on a Barreto–Naehrig elliptic curve. Our library is able to compute the optimal ate pairing over a 254bit prime field
$\mathbb{F}_{p}$
, in just 2.33 million of clock cycles on a single core of an Intel Core i7 2.8GHz processor, which implies that the pairing computation takes 0.832msec. We are able to achieve this performance by a careful implementation of the base field arithmetic through the usage of the customary Montgomery multiplier for prime fields. The prime field is constructed via the Barreto–Naehrig polynomial parametrization of the prime p given as, p = 36t^{4} + 36t^{3} + 24t^{2} + 6t + 1, with t = 2^{62} − 2^{54} + 2^{44}. This selection of t allows us to obtain important savings for both the Miller loop as well as the final exponentiation steps of the optimal ate pairing.
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By
Gafni, Eli; Rajsbaum, Sergio
11 Citations
In roundbyround models of distributed computing processes run in a sequence of (synchronous or asynchronous) rounds. The advantage of the roundbyround approach is that invariants established in the first round are preserved in later rounds. An elegant asynchronous roundbyround shared memory model, is the iterated snapshots model (IS). Instead of the snapshots model where processes share an array m[·] that can be accessed any number of times, indexed by process ID, where P_{i} writes to m[i] and can take a snapshot of the entire array, we have processes share a twodimensional array m[·,·], indexed by iteration number and by process ID, where P_{i} in iteration r writes once to m[r, i] and takes one snapshot of row r, m[r,·]. The IS model lends itself more easily to combinatorial analysis. However, to show that whenever a task is impossible in the IS model the task is impossible in the snapshots model, a simulation is needed. Such a simulation was presented by Borowsky and Gafni in PODC97; namely, it was shown how to take a waitfree protocol for the snapshots model, and transform it into a protocol for the IS model, solving the same task.
In this paper we present a new simulation from the snapshots model to the IS model, and show that it can be extended to work with models stronger that waitfree. The main contribution is to show that the simulation can work with models that have access to certain communication objects, called 01tasks. This extends the result of Gafni, Rajsbaum and Herlihy in DISC’2006 stating that renaming is strictly weaker than set agreement from the IS model to the usual noniterated waitfree read/write shared memory model.
We also show that our simulation works with tresilient models and the more general dependent process failure model of Junqueira and Marzullo. This version of the simulation extends previous results by Herlihy and Rajsbaum in PODC’2010 and DISC’2010 about the topological connectivity of a protocol complex in an iterated dependent process failure model, to the corresponding noniterated model.
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By
Czyzowicz, Jurek; Kranakis, Evangelos; Krizanc, Danny; Lambadaris, Ioannis; Narayanan, Lata; Opatrny, Jaroslav; Stacho, Ladislav; Urrutia, Jorge; Yazdani, Mohammadreza
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30 Citations
A set of sensors establishes barrier coverage of a given line segment if every point of the segment is within the sensing range of a sensor. Given a line segment I, n mobile sensors in arbitrary initial positions on the line (not necessarily inside I) and the sensing ranges of the sensors, we are interested in finding final positions of sensors which establish a barrier coverage of I so that the sum of the distances traveled by all sensors from initial to final positions is minimized. It is shown that the problem is NP complete even to approximate up to constant factor when the sensors may have different sensing ranges. When the sensors have an identical sensing range we give several efficient algorithms to calculate the final destinations so that the sensors either establish a barrier coverage or maximize the coverage of the segment if complete coverage is not feasible while at the same time the sum of the distances traveled by all sensors is minimized. Some open problems are also mentioned.
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By
SolorioFernández, Saúl; CarrascoOchoa, J. Ariel; MartínezTrinidad, José Fco.
4 Citations
In this paper, we introduce a new hybrid filterwrapper method for supervised feature selection, based on the Laplacian Score ranking combined with a wrapper strategy. We propose to rank features with the Laplacian Score to reduce the search space, and then we use this order to find the best feature subset. We compare our method against other based on ranking feature selection methods, namely, Information Gain Attribute Ranking, Relief, Correlationbased Feature Selection, and additionally we include in our comparison a Wrapper Subset Evaluation method. Empirical results over ten realworld datasets from the UCI repository show that our hybrid method is competitive and outperforms in most of the cases to the other feature selection methods used in our experiments.
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By
Arias Montaño, Alfredo; Coello Coello, Carlos A.; MezuraMontes, Efrén
This paper introduces a novel Parallel MultiObjective Evolutionary Algorithm (pMOEA) which is based on the island model. The serial algorithm on which this approach is based uses the differential evolution operators as its search engine, and includes two mechanisms for improving its convergence properties (through local dominance and environmental selection based on scalar functions). Two different parallel approaches are presented. The first aims at improving effectiveness (i.e., for better approximating the Pareto front) while the second aims to provide a better efficiency (i.e., by reducing the execution time through the use of small population sizes in each subpopulation). To assess the performance of the proposed algorithms, we adopt a set of standard test functions and performance measures taken from the specialized literature. Results are compared with respect to its serial counterpart and with respect to three algorithms representative of the stateoftheart in the area: NSGAII, MOEA/D and MOEA/DDE.
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By
Rajsbaum, Sergio
5 Citations
In centralized computing we can compute a function composing a sequence of elementary functions, where the output of the ith function in the sequence is the input to the i + 1st function in the sequence. This computation is done without persistent registers that could store information of the outcomes of these function invocations. In distributed computing, a task is the analogue of a function. An iterated model is defined by some base set of tasks. Processes invoke a sequence of tasks from this set. Each process invokes the i + 1st task with its output from the ith task. Processes access the sequence of tasks, onebyone, in the same order, and asynchronously. Any number of processes can crash. In the most basic iterated model the base tasks are read/write registers. Previous papers have studied this and other iterated models with more powerful base tasks or enriched with failure detectors, which have been useful to prove impossibility results and to design algorithms, due to the elegant recursive structure of the runs. This talk surveys results in this area, contributed mainly by Borowsky, Gafni, Herlihy, Raynal, Travers and the author.
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By
Gafni, Eli; Rajsbaum, Sergio
15 Citations
The benefits of developing algorithms via recursion are well known. However, little use of recursion has been done in distributed algorithms, in spite of the fact that recursive structuring principles for distributed systems have been advocated since the beginning of the field. We present several distributed algorithms in a recursive form, which makes them easier to understand and analyze. Also, we expose several interesting issues arising in recursive distributed algorithms. Our goal is to promote the use and study of recursion in distributed algorithms.
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By
Peña Ayala, Alejandro; Sossa, Humbero
2 Citations
In this paper an approach oriented to acquire, depict, and administrate knowledge about the student is proposed. Moreover, content is also characterized to describe lectures. In addition, the work focuses on the semantics of the attributes that reveal a profile of the student and the teaching experiences. The meaning of such properties is stated as an ontology. Thus, inheritance and causal inferences are made. According to the semantics of the attributes and the conclusions induced, the sequencing module of a Webbased educational system (WBES) delivers the appropriate option of lecture to students. The underlying hypothesis is: the apprenticeship of students is enhanced when a WBES understands the nature of the content and the student’s characteristics. Based on the empirical evidence outcome by a trial, it is concluded that: Successful WBES account the knowledge that describe their students and lectures.
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By
DomínguezMedina, Christian; CruzCortés, Nareli
2 Citations
Wireless Sensor Networks have become an active research topic in the last years. The routing problem is a very important part in this kind of networks that need to be considered in order to maximize the network life time. As the size of the network increases, routing becomes more complex due the amount of sensor nodes in the network. Sensor nodes in Wireless Sensor Networks are very constrained in memory capabilities, processing power and batteries. Ant Colony Optimization based routing algorithms have been proposed to solve the routing problem trying to deal with these constrains. We present a comparison of two Ant Colonybased routing algorithms, taking into account current amounts of energy consumption under different scenarios and reporting the usual metrics for routing in wireless sensor networks.
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By
Cabrera, Juan Carlos Fuentes; Coello, Carlos A. Coello
8 Citations
In this chapter, we present a multiobjective evolutionary algorithm (MOEA) based on the heuristic called “particle swarm optimization” (PSO). This multiobjective particle swarm optimizer (MOPSO) is characterized for using a very small population size, which allows it to require a very low number of objective function evaluations (only 3000 per run) to produce reasonably good approximations of the Pareto front of problems of moderate dimensionality. The proposed approach first selects the leader and then selects the neighborhood for integrating the swarm. The leader selection scheme adopted is based on Pareto dominance and uses a neighbors density estimator. Additionally, the proposed approach performs a reinitialization process for preserving diversity and uses two external archives: one for storing the solutions that the algorithm finds during the search process and another for storing the final solutions obtained. Furthermore, a mutation operator is incorporated to improve the exploratory capabilities of the algorithm. The proposed approach is validated using standard test functions and performance measures reported in the specialized literature. Our results are compared with respect to those generated by the Nondominated Sorting Genetic Algorithm II (NSGAII), which is a MOEA representative of the stateoftheart in the area.
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By
Jaimes, Antonio López; Aguirre, Hernán; Tanaka, Kiyoshi; Coello Coello, Carlos A.
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1 Citations
Here, we present a partition strategy to generate objective subspaces based on the analysis of the conflict information obtained from the Pareto front approximation found by an underlying multiobjective evolutionary algorithm. By grouping objectives in terms of the conflict among them, we aim to separate the multiobjective optimization into several subproblems in such a way that each of them contains the information to preserve as much as possible the structure of the original problem. The ranking and parent selection is independently performed in each subspace. Our experimental results show that the proposed conflictbased partition strategy outperforms NSGAII in all the test problems considered in this study. In problems in which the degree of conflict among the objectives is significantly different, the conflictbased strategy achieves its best performance.
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By
Herlihy, Maurice; Rajsbaum, Sergio
3 Citations
Roughly speaking, a simplicial complex is shellable if it can be constructed by gluing a sequence of nsimplexes to one another along (n − 1)faces only. Shellable complexes have been studied in the combinatorial topology literature because they have many nice properties.
It turns out that many standard models of concurrent computation can be captured either as shellable complexes, or as the simple union of shellable complexes. We consider general adversaries in the synchronous, asynchronous, and semisynchronous messagepassing models, as well as asynchronous shared memory augmented by consensus and set agreement objects.
We show how to exploit their common shellability structure to derive new and remarkably succinct tight (or nearly so) lower bounds on connectivity of protocol complexes and hence on solutions to the kset agreement task in these models.
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By
Hernández, Yasmín; Sucar, Luis Enrique; ArroyoFigueroa, Gustavo
2 Citations
We are developing an affective model for intelligent tutoring systems; thus, the tutor considers the affective state as well as the knowledge state of the student to give instruction to students. An important component of the affective model is the affective student model. This last one is rooted on the OCC cognitive model of emotions and the fivefactor model, and it is represented as a dynamic Bayesian network. The personality traits, goals and knowledge state are considered to establish the student affect. The affective model has been integrated to an intelligent learning environment for learning mobile robotics. We conducted an initial evaluation of the affective student model with a group of 20 under graduate and graduate students to evaluate the affective student model. Results are encouraging since they show a high agreement between the affective state established by the affective student model and the affective state reported by the students.
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By
Meza, Ricardo Rafael Quintero; Sánchez, Leopoldo Zenaido Zepeda; Zazueta, Liliana Vega
One of the main success factors of the business IT infrastructure is its capacity to face the change. Many companies are defining its IT infrastructure based on ServiceOriented Architecture (SOA), which promises flexibility and efficiency to face the change by reusing and composing loosely coupled services. Because the actual technological platforms used to build SOA systems were not defined originally to this kind of systems, the majority of existing tools for service composition demands that the programmer knows a lot of technical details for its implementation. In this article we propose a conceptual modeling solution to both problems based on the ModelDriven Architecture. Our solution proposes the specification of services and its reuse in terms of platform independent conceptual models. These models are then transformed into platform specific models by a set of ModeltoModel transformation rules, and finally the source code is generated by a set of ModeltoText transformation rules. Our proposal has been implemented with a tool implemented using the Eclipse Modeling Framework using QVT and Mofscript model transformation languages.
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By
Garza Fabre, Mario; Toscano Pulido, Gregorio; Coello Coello, Carlos A.
10 Citations
Pareto dominance (PD) has been the most commonly adopted relation to compare solutions in the multiobjective optimization context. Multiobjective evolutionary algorithms (MOEAs) based on PD have been successfully used in order to optimize biobjective and threeobjective problems. However, it has been shown that Pareto dominance loses its effectiveness as the number of objectives increases and thus, the convergence behavior of approaches based on this concept decreases. This paper tackles the MOEAs’ scalability problem that arises as we increase the number of objective functions. In this paper, we perform a comparative study of some of the stateoftheart fitness assignment methods available for multiobjective optimization in order to analyze their ability to guide the search process in highdimensional objective spaces.
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By
Monroy, Raúl
In the formal methods approach to software verification, we use logical formulae to model both the program and its intended specification, and, then, we apply (automated) reasoning techniques to demonstrate that the formulae satisfy a verification conjecture. One may either apply proving techniques, to provide a formal verification argument, or disproving techniques to falsify the verification conjecture. However, programs often contain bugs or are flawed, and, so, the verification process breaks down. Interpreting the failed proof attempt or the counterexample, if any, is very valuable, since it potentially helps identifying the program bug or flaw. Lakatos, in his book Proofs and Refutations, argues that the analysis of a failed proof often holds the key for the development of a theory. Proof analysis enables the strengthening of naïve conjectures and concepts, without severely weakening its content. In this paper, we survey our encounters on the productive use of failure in the context of a few theories, natural numbers and (higherorder) lists, and in the context of security protocols.
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By
GonzalezHernandez, Loreto; RangelValdez, Nelson; TorresJimenez, Jose
8 Citations
The development of a new system involves extensive tests on the software functionality in order to identify possible failures. Also, a system already built requires a fine tuning of its configurable options to give the best performance in the environment it is going to work. Both cases require a finite set of tests that avoids testing all the possible combinations (which is time consuming); to this situation Mixed Covering Arrays (MCAs) are a feasible alternative. MCAs are combinatorial structures represented as matrices having a test case per row. MCAs are small, in comparison with brute force, and guarantees a level of interaction among the parameters involved (a difference with random testing). We present a Tabu Search (TS) algorithm to construct MCAs; the novelty in the algorithm is a mixture of three neighborhood functions. We also present a new benchmark for the MCAs problem. The experimental evidence showed that the TS algorithm improves the results obtained by other approaches reported in the literature, finding the optimal solution in some the solved cases.
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By
ZatarainCabada, Ramón; BarrónEstrada, M. L.; Angulo, Viridiana Ponce; García, Adán José; García, Carlos A. Reyes
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4 Citations
In this paper, we present a framework used for creating, training, and testing SOM neural networks, which are used to recognize student learning styles under different pedagogical models. The SOMs are part of the student model of Intelligent Tutoring Systems we implemented for mobile devices and Webbased Learning Systems. The main contribution of this paper is the framework to build SOMs which can be used with any pedagogical model of learning styles. The SOM network produced with our framework has been tested with mobile devices and a system of webbased learning.
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By
SanchezDiaz, Guillermo; PizaDavila, Ivan; LazoCortes, Manuel; MoraGonzalez, Miguel; SalinasLuna, Javier
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4 Citations
Typical testors are a useful tool for both feature selection and for determining feature relevance in supervised classification problems. Nowadays, generating all typical testors of a training matrix is computationally expensive; all reported algorithms have exponential complexity, depending mainly on the number of columns in the training matrix. For this reason, different approaches such as sequential and parallel algorithms, genetic algorithms and hardware implementations techniques have been developed. In this paper, we introduce a fast implementation of the algorithm CT_EXT (which is one of the fastest algorithms reported) based on an accumulative binary tuple, developed for generating all typical testors of a training matrix. The accumulative binary tuple implemented in the CT_EXT algorithm, is a useful way to simplifies the search of feature combinations which fulfill the testor property, because its implementation decreases the number of operations involved in the process of generating all typical testors. In addition, experimental results using the proposed fast implementation of the CT_EXT algorithm and the comparison with other state of the art algorithms that generated typical testors are presented.
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By
Leguizamón, Guillermo; Coello, Carlos A. Coello
4 Citations
The Ant Colony Optimization (ACO) metaheuristic embodies a large set of algorithms which have been successfully applied to a wide range of optimization problems. Although ACO practitioners have a long tradition in solving combinatorial optimization problems, many other researchers have recently developed a variety of ACO algorithms for dealing with continuous optimization problems. One of these algorithms is the socalled ACO
$_{\Bbb{R}}$
, which is one of the most relevant ACO algorithms currently available for continuous optimization problems. Although ACO
$_{\Bbb{R}}$
has been found to be successful, to the authors’ best knowledge its use in highdimensionality problems (i.e., with many decision variables) has not been documented yet. Such problems are important, because they tend to appear in realworld applications and because in them, diversity loss becomes a critical issue. In this paper, we propose an alternative ACO
$_{\Bbb{R}}$
algorithm (DACO
$_{\Bbb{R}}$
) which could be more appropriate for large scale unconstrained continuous optimization problems. We report the results of an experimental study by considering a recently proposed test suite. In addition, the parameters setting of the algorithms involved in the experimental study are tuned using an ad hoc tool. Our results indicate that our proposed DACO
$_{\Bbb{R}}$
is able to improve both, the quality of the results and the computational time required to achieve them.
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By
Pinto, David; Vilariño, Darnes; Balderas, Carlos; Tovar, Mireya; Beltrán, Beatriz
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1 Citations
Word Sense Disambiguation (WSD) is considered one of the most important problems in Natural Language Processing [1]. It is claimed that WSD is essential for those applications that require of language comprehension modules such as search engines, machine translation systems, automatic answer machines, second life agents, etc. Moreover, with the huge amounts of information in Internet and the fact that this information is continuosly growing in different languages, we are encourage to deal with crosslingual scenarios where WSD systems are also needed. On the other hand, Lexical Substitution (LS) refers to the process of finding a substitute word for a source word in a given sentence. The LS task needs to be approached by firstly disambiguating the source word, therefore, these two tasks (WSD and LS) are somehow related. In this paper, we present a naïve approach to tackle the problem of crosslingual WSD and crosslingual lexical substitution. We use a bilingual statistical dictionary, which is calculated with Giza++ by using the EUROPARL parallel corpus, in order to calculate the probability of a source word to be translated to a target word (which is assumed to be the correct sense of the source word but in a different language). Two versions of the probabilistic model are tested: unweighted and weighted. The results were compared with those of an international competition, obtaining a good performance.
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By
Estrada, Hugo; Martínez, Alicia; Pastor, Oscar; Mylopoulos, John; Giorgini, Paolo
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6 Citations
Nowadays, there is wide consensus on the importance of organizational modelling in the definition of software systems that correctly address business needs. Accordingly, there exist many modelling techniques that capture business semantics from different perspectives: transactional, goaloriented, aspectoriented, valueoriented etc. However, none of these proposals accounts for the service nature of most business organizations, nor of the growing importance of service orientation in computing. In this paper, an overview of a new business serviceoriented modeling approach, that extends the i* framework, is presented as a solution to this problem. The proposed modeling approach enables analysts to represent an organizational model as a composition of business services, which are the basic building blocks that encapsulate a set of business process models. In these models the actors participate in actor dependency networks through interfaces defined in the business service specification.
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By
Escarcega, David; Ramos, Fernando; Espinosa, Ana; Berumen, Jaime
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Cervical Cancer (CC) is the result of the infection of high risk Human Papilloma Viruses. mRNA microarray expression data provides biologists with evidences of cellular compensatory gene expression mechanisms in the CC progression. Pattern recognition of signalling pathways through expression data can reveal interesting insights for the understanding of CC. Consequently, gene expression data should be submitted to different preprocessing tasks. In this paper we propose a methodology based on the integration of expression data and signalling pathways as a needed phase for the pattern recognition within signaling CC pathways. Our results provide a topdown interpretation approach where biologists interact with the recognized patterns inside signalling pathways.
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By
Hernández, Constanza; Quintero, Ricardo; Sánchez, Leopoldo Z.
The present work is done within the framework of the Model Driven Development of Software. It proposes an initial strategy for obtaining a metamodel that captures the main elements (objects, actors, activities, subjects, relations, etc.) that characterize the Web applications of Social Networks. It also includes the definition of a tool that allows the graphical edition of models for the mentioned applications, considering as the base for capturing the requirements of the main elements of the Social Network application. With these models, a general automatic code generation strategy for a Web 2.0 application is presented.
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By
CruzSantos, William; MoralesLuna, Guillermo
1 Citations
We study the Hamiltonians resulting from the Adiabatic Quantum Computing treatment of the Satisfiability Problem SAT. We provide respective procedures for explicit calculation of the involved Hamiltonians. The statement of the ending Hamiltonians allows us to pose a variant of SAT which is also NPcomplete.
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By
Gelbukh, Alexander; Kolesnikova, Olga
4 Citations
The meaning of word combination such as give a book or lend money can be obtained by mechanically combining the meaning of the two constituting words: to give is to hand over, a book is a pack of pages, then to give a book is to hand over a pack of pages. However, the meaning of such word combinations as give a lecture or lend support is not obtained in this way: to give a lecture is not to hand it over. Such word pairs are called collocations. While their meaning cannot be derived automatically from the meaning of their constituents, we show how to predict the meaning of a previously unseen word combination using semantic regularities we observe in a training set of collocations whose meaning has been specified manually.
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By
Flores, Juan J.; Rodriguez, Hector; Graff, Mario
3 Citations
Evolutionary design of time series predictors is a field that has been explored for several years now. The levels of design vary in the many works reported in the field. We decided to perform a complete design and training of ARIMA models using Evolutionary Computation. This decision leads to high dimensional search spaces, whose size increases exponentially with dimensionality. In order to reduce the size of those search spaces we propose a method that performs a preliminary statistical analysis of the inputs involved in the model design and their impact on quality of results; as a result of the statistical analysis, we eliminate inputs that are irrelevant for the prediction task. The proposed methodology proves to be effective and efficient, given that the results increase in accuracy and the computing time required to produce the predictors decreases.
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By
Pinto, David; Tovar, Mireya; Vilariño, Darnes; Beltrán, Beatriz; JiménezSalazar, Héctor; Campos, Basilia
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The aim of this paper is to use unsupervised classification techniques in order to group the documents of a given huge collection into clusters. We approached this challenge by using a simple clustering algorithm (KStar) in a recursive clustering process over subsets of the complete collection.
The presented approach is a scalable algorithm which may automatically discover the number of clusters. The obtained results outperformed different baselines presented in the INEX 2009 clustering task.
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By
Gomez, Laura E.; Sossa, Humberto; Barron, Ricardo; Jimenez, Julio F.
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A new method for the retrieval of melodies from a database is described in this paper. For its functioning, the method makes use of Dynamic Neural Networks (DNN). During training a set ofDNN is first trained with information of the melodies to be retrieved. Instead of using traditional signal descriptors we use the matrix of synaptic weights that can be efficiently used for melody representation and retrieval. Most of the reported works have been focused on the symbolic representation of musical information. None of them have provided good results with original signals.
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By
Etcheverry, Gibran; LópezDamian, Efraín; ReyesGarcía, Carlos A.
1 Citations
Newborn cry analysis is a difficult task due to its nonstationary nature, combined to the presence of nonlinear behavior as well. Therefore, an adaptive hereditary optimization algorithm is implemented in order to avoid the use of windowing nor overlapping to capture the transient signal behavior. Identification of the linear part of this particular time series is carried out by employing an Autorregresive Moving Average (ARMA) structure; then, the resultant estimation error is approched by a Nonlinear Autorregresive Moving Average (NARMA) model, which realizes a Volterra cubic kernel by means of a bilinear homogeneous structure in order to capture burst behavior. Normal, deaf, asfixia, pain, and uncommon newborn cries are inspected for differentation.
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By
Alanis, Alma Y.; Sanchez, Edgar N.; Loukianov, Alexander G.; PerezCisneros, Marco A.
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1 Citations
This paper deals with the problem of discretetime nonlinear system identification via Recurrent High Order Neural Networks. It includes the respective stability analysis on the basis of the Lyapunov approach for the extended Kalman filter (EKF)based NN training algorithm, which is applied for learning. Applicability of the scheme is illustrated via simulation for a discretetime nonlinear model of an electric induction motor.
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By
MartínezDíaz, Saúl; CarmonaTroyo, Javier A.
Fingerprint recognition has been used from many years for identification of persons. However, conventional fingerprint recognition systems might fail with poor quality, noisy or rotated images. Recently, novel nonlinear composite filters for correlationbased pattern recognition have been introduced. The filters are designed with information from distorted versions of reference object to achieve distortioninvariant recognition. Besides, a nonlinear correlation operation is applied among the filter and the test image. These kinds of filters are robust to nonGaussian noise. In this paper we apply nonlinear composite filters for fingerprint verification. Computer simulations show performance of proposed filters with distorted fingerprints. In addition, in order to illustrate robustness to noise, filters were tested with noisy images.
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By
Pazos R., Rodolfo A.; Rojas P., Juan C.; Santaolaya S., René; Martínez F., José A.; Gonzalez B., Juan J.
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3 Citations
A query written in natural language (NL) may involve several linguistic problems that cause a query not being interpreted or translated correctly into SQL. One of these problems is implicit information or semantic ellipsis, which can be understood as the omission of important words in the wording of a query written in NL. An exhaustive survey on NLIDB works has revealed that most of these works has not systematically dealt with semantic ellipsis. In experiments conducted on commercial NLIDBs, very poor results have been obtained (7% to 16.9%) when dealing with query corpora that involve semantic ellipsis. In this paper we propose a dialogue manager (DM) for a NLIDB for solving semantic ellipsis problems. The operation of this DM is based on a typification of elliptical problems found in queries, which permits to systematically deal with this problem. Additionally, the typification has two important characteristics: domain independence, which permits the typification to be applied to queries of different databases, and generality, which means that it holds for different languages such as English, French, Italian, Spanish, etc. These characteristics are inherited to the dialogue processes implemented in the DM, since they are based on this typification. In experiments conducted with this DM and a NLIDB on a corpus of elliptical queries, an increase of correctly answered queries of 3035% was attained.
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By
Ríos Gaona, Miguel Angel; Gelbukh, Alexander; Bandyopadhyay, Sivaji
1 Citations
In this paper we propose a new causeeffect nonsymmetric measure applied to the task of Recognizing Textual Entailment .First we searched over a big corpus for sentences which contains the discourse marker “because” and collected causeeffect pairs. The entailment recognition is based on measure the causeeffect relation between the text and the hypothesis using the relative frequencies of words from the causeeffect pairs. Our measure outperformed the baseline method, over the three test sets of the PASCAL Recognizing Textual Entailment Challenges (RTE). The measure shows to be good at discriminate over the “true” class. Therefore we develop a metaclassifier using a symmetric measure and a nonsymmetric measure as base classifiers. So, our metaclassifier has a competitive performance.
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By
Conomikes, John; Pacheco, Zachary; Barrera, Salvador; Cantu, Juan Antonio; Gomez, Lucy Beatriz; Reyes, Christian; Mendez Villarreal, Juan Manuel; Shime, Takao; Kamiya, Yuki; Kawai, Hideki; Kunieda, Kazuo; Yamada, Keiji
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“Remote Context Monitoring of Actions and Behavior in a Location Through the Usage of a 3D Visualization in Realtime” is a software application designed to read large amounts of data from a database and use that data to recreate the context that events occurred to improve understanding of the data.
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By
GonzalezAguirre, David; Asfour, Tamim; BayroCorrochano, Eduardo; Dillmann, Ruediger
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3 Citations
A novel modelbased approach for global selflocalization using active stereo vision and density Gaussian spheres is presented. The proposed object recognition components deliver noisy percept subgraphs, which are filtered and fused into an egocentered reference frame. In subsequent stages, the required visiontomodel associations are extracted by selecting egopercept subsets in order to prune and match the corresponding worldmodel subgraph. Ideally, these coupled subgraphs hold necessary information to obtain the modeltoworld transformation, i.e., the pose of the robot. However, the estimation of the pose is not robust due to the uncertainties introduced when recovering Euclidean metric from images and during the mapping from the camera to the egocenter. The approach models the uncertainty of the percepts with a radial normal distribution. This formulation allows a closedform solution which not only derives the maximal density position depicting the optimal egocenter but also ensures the solution even in situations where pure geometric spheres might not intersect.
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By
Martínez, Genaro J.; Morita, Kenichi; Adamatzky, Andrew; Margenstern, Maurice
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1 Citations
We study Lifelike cellular automaton rule B2/S2345. This automaton exhibits a chaotic behavior yet capable for purposeful computation. The automaton implements Boolean gates via patterns which compete for the space when propagate in channels. Values of Boolean variables are encoded into two types of patterns — symmetric (False) and asymmetric (True). We construct basic logical gates and elementary arithmetical circuits by simulating logical signals using glider reactions taking place in the channels built of nondestructible still lifes. We design a binary adder of majority gates realised in rule B2/S2345.
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By
McIntosh, Harold V.
4 Citations
The de Bruijn diagram describing those decompositions of the neighborhoods of a one dimensional cellular automaton which conform to predetermined requirements of periodicity and translational symmetry shows how to construct extended configurations satisfying the same requirements. Similar diagrams, formed by stages, describe higher dimensional automata, although they become more laborious to compute with increasing neighborhood size. The procedure is illustrated by computing some still lifes for Conway’s game of Life, a widely known two dimensional cellular automaton. This paper is written in September 10, 1988.
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By
Martínez, Saúl Zapotecas; Coello Coello, Carlos A.
3 Citations
The development of multiobjective evolutionary algorithms (MOEAs) assisted by metamodels has increased in the last few years. However, the use of local search engines assisted by metamodels for multiobjective optimization has been less common in the specialized literature. In this paper, we propose the use of a local search mechanism which is assisted by a metamodel based on support vector machines. The local search mechanism adopts a freederivative mathematical programming technique and consists of two main phases: the first generates approximations of the Pareto optimal set. Such solutions are obtained by solving a set of aggregating functions which are defined by different weighted vectors. The second phase generates new solutions departing from those obtained during the first phase. The solutions found by the local search mechanism are incorporated into the evolutionary process of our MOEA. Our experiments show that our proposed approach can produce good quality results with a budget of only 1,000 fitness function evaluations in test problems having between 10 and 30 decision variables.
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ZamudioFuentes, Luis M.; GarcíaVázquez, Mireya S.; RamírezAcosta, Alejandro A.
1 Citations
Eyelashes and reflections occluding the iris region are noise factors that degrade the performance of iris recognition. If these factors are not eliminated in iris segmentation phase, they are incorrectly considered as the iris region. Thus, produce false iris pattern information which decreases the recognition rate. In this paper a statistical approach is used to improve iris segmentation phase eliminating this noise from none constrain images, which is composed in three parts, finding the pupil and limbus boundary, reflection detection and eyelash detection. First an edge map is calculated using canny filter then the Circular Hough Transform is used to improve circle parameter finding. An intensity variation analysis is use to recognize a strong reflection. Eyelashes are classified in two categories, separable and multiple. Intensity variances are used to detect multiple eyelashes and an edge detector to localize separable eyelashes. The results show that statistics are useful to decide when is necessary applied the eyelash detector.
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Morales, Jorge; Gonzalez, Jesus A.; ReyesGarcia, Carlos A.; Altamirano, Leopoldo
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Most of the current satellite image classification methods consider rough boundaries among homogeneous regions. However; real images contain transition regions where pixels belong, at different degrees, to different classes. With this motivation, in this paper we propose a satellite image classification method that allows the identification of transition regions among homogeneous regions. Our solution is based on Soft Computing because of its ability to handle the uncertainties present in nature. We present our method as well as preliminary results that show how our method is able to solve real world problems.
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OrdoñezSalinas, Sonia; Gelbukh, Alexander
We argue for that taking into account semantic relations between words in the text can improve information retrieval performance. We implemented the process of information retrieval with simplified Conceptual Graphlike structures and compare the results with those of the vector space model. Our semantic representation, combined with a small simplification of the vector space model, gives better results. In order to build Conceptual Graphlike representation, we have developed a grammar based on the dependency formalism and the standard defined for Conceptual Graphs (CG). We used noun premodifiers and noun postmodifiers, as well as verb frames, extracted from VerbNet, as a source of definition of semantic roles. VerbNet was chosen since its definitions of semantic roles have much in common with the CG standard. We experimented on a subset of the ImageClef 2008 collection of titles and annotations of medical images.
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Hernández, Manuel
2 Citations
This paper presents a taxonomy of some exact, righttoleft, stringmatching algorithms. The taxonomy is based on results obtained by using logic program transformation over a naive and nondeterministic specification. A derivation of the search part and some notes about the preprocessing part of each algorithm is presented. The derivations show several design decisions behind each algorithm, and allow us to organize the algorithms within a taxonomic tree, giving us a better understanding of these algorithms and possible mechanical procedures to derive them.
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Cortés Antonio, Prometeo; Batyrshin, Ildar; Molina Lozano, Heron; Villa Vargas, Luis A.; Rudas, Imre
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2 Citations
A method of FPGA implementation of fuzzy system with parametric membership functions and conjunctions is proposed. The implemented system is based on a Sugeno fuzzy model with two input variables. Fuzzy sets in the premises of the rules are given by parametric triangular membership functions and conjunction operations are defined by parametric (p)monotone sum of basic tnorms. The paper presents the hardware design of a 8bit configurable fuzzy system, implemented on the DE2 development board from Altera using VHDL language.
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Morán, Alberto L.; RodríguezCovili, Juan; Mejia, David; Favela, Jesus; Ochoa, Sergio
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4 Citations
Social networking systems allow users to keep in touch with relatives and friends in the absence of physical proximity. These tools are also increasingly supporting productive interactions in diverse working environments. In this paper, based on the understanding of informal communication in hospitals, we identify opportunities for the use of social networking software in support of hospital work. This has inspired the design of meetU, a tool aimed at supporting impromptu social networking through an adhoc communication infrastructure. The services offered by the system are illustrated through interaction scenarios, which were also used to evaluate the system with a group of medical interns.
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Calleja, Jorge; Huerta, Gladis; Fuentes, Olac; Benitez, Antonio; Domínguez, Eduardo López; Medina, Ma. Auxilio
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In this paper we present an experimental study of the performance of six machine learning algorithms applied to morphological galaxy classification. We also address the learning approach from imbalanced data sets, inherent to many realworld applications, such as astronomical data analysis problems. We used two oversampling techniques: SMOTE and Resampling, and we vary the amount of generated instances for classification. Our experimental results show that the learning method Random Forest with Resampling obtain the best results for three, five and seven galaxy types, with a Fmeasure about .99 for all cases.
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RamírezdelaRosa, Gabriela; MontesyGómez, Manuel; VillaseñorPineda, Luis; PintoAvendaño, David; Solorio, Thamar
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1 Citations
Crosslingual text classification consists of exploiting labeled documents in a source language to classify documents in a different target language. In addition to the evident translation problem, this task also faces some difficulties caused by the cultural discrepancies manifested in both languages by means of different topic distributions. Such discrepancies make the classifier unreliable for the categorization task. In order to tackle this problem we propose to improve the classification performance by using information embedded in the own target dataset. The central idea of the proposed approach is that similar documents must belong to the same category. Therefore, it classifies the documents by considering not only their own content but also information about the assigned category to other similar documents from the same target dataset. Experimental results using three different languages evidence the appropriateness of the proposed approach.
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RamirezPadron, Ruben; Foregger, David; Manuel, Julie; Georgiopoulos, Michael; Mederos, Boris
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2 Citations
Outlier detection is an important research topic that focuses on detecting abnormal information in data sets and processes. This paper addresses the problem of determining which class of kernels should be used in a geometric framework for nearest neighborbased outlier detection. It introduces the class of similarity kernels and employs it within that framework. We also propose the use of isotropic stationary kernels for the case of normed input spaces. Two definitions of similarity scores using kernels are given: the kNN kernel similarity score (kNNSS) and the summation kernel similarity score (SKSS). The paper concludes with preliminary experimental results comparing the performance of kNNSS and SKSS for outlier detection on four data sets. SKSS compared favorably to kNNSS.
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Ramos, J. Guadalupe; Silva, Josep; Arroyo, Gustavo; Solorio, Juan C.
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2 Citations
In this work, we introduce a new method for information extraction from the semantic web. The fundamental idea is to model the semantic information contained in the microformats of a set of web pages, by using a data structure called semantic network. Then, we introduce a novel technique for information extraction from semantic networks. In particular, the technique allows us to extract a portion—a slice—of the semantic network with respect to some criterion of interest. The slice obtained represents relevant information retrieved from the semantic network and thus from the semantic web. Our approach can be used to design novel tools for information retrieval and presentation, and for information filtering that was distributed along the semantic web.
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MartinezPena, Jorge; TorresJimenez, Jose; RangelValdez, Nelson; AvilaGeorge, Himer
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6 Citations
This paper presents a simulated annealing (SA) algorithm for the construction of ternary covering arrays (CAs) using a trinomial coefficient representation. A ternary CA, denoted by CA(t,k,3), is an N ×k array where each N ×t subarray contains each of the 3^{t} combinations of symbols at least once. The construction of optimal CAs is, in general, an NPcomplete problem. Many reported SA implementations use an N ×k matrix representation for the CA construction. Instead of this, we represent ternary CAs using trinomial coefficients in order to reduce the search space for the SA algorithm.
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By
Decouchant, Dominique; Mendoza, Sonia; Rodríguez, José
Researches and efforts currently being developed within the World Wide Web environment, whose aim is to provide cooperative supports, are mainly performed in the field of the “Semantic Web”. These efforts are based on technological components such as the XML, RDF, and OWL languages that allow the cooperative definition of distributed ontologies. From these components, it is possible to develop “reasoning” programs that are able a) to infer information from data described with these languages and b) to exploit the defined ontologies. Moreover, programs may also be defined to provide supports to collaborators to cooperatively exploit the defined ontologies. However, all theses efforts remain developed at the application level. Thus, no suited distributed support for Web cooperative work had been investigated that deals with the unreliability of such a distributed environment.
In this chapter, we present the PIÑAS infrastructure which provides means for supporting cooperative work on the Web. Using cooperative applications that are built employing the services of this infrastructure, several users can access and modify replicated shared entities in a consistent and controlled way. PIÑAS provides suited features, such as: user identification, multisite user definition, user and entity naming, shared entity fragmentation and replication, storage, consistency, and automatic distributed updating. We propose seamless extensions to standard Web services that can be fully integrated within the Web environment. Moreover, the innovative PIÑAS features provide reliable support for temporarily disconnected and nomadic work.
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Confalonieri, Roberto; Nieves, Juan Carlos; Osorio, Mauricio; VázquezSalceda, Javier
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5 Citations
Logic programs with ordered disjunction (or LPODs) have shown to be a flexible specification language able to model and reason about preferences in a natural way. However, in some realistic applications which use user preferences in the reasoning, information can be pervaded with vagueness and a preferenceaware reasoning process that can handle uncertainty is required. In this paper we address these issues, and we propose a framework which combines LPODs and possibilistic logic to be able to deal with a reasoning process that is preferenceaware, nonmonotonic, and uncertain. We define a possibilistic semantics for capturing logic programs with possibilistic ordered disjunction (or LPPODs) which is a generalization of the original semantics. Moreover, we present several transformation rules which can be used to optimize LPODs and LPPODs code and we show how the semantics of LPODs and the possibilistic semantics of LPPODs are invariant w.r.t. these transformations.
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ZatarainCabada, Ramón; BarrónEstrada, M. L.; Angulo, Viridiana Ponce; García, Adán José; García, Carlos A. Reyes
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3 Citations
The implementation of an adaptive learning social network to be used as an authoring tool, is presented in this paper. With this tool, adaptive courses, intelligent tutoring systems and lessons can be created, displayed and shared in collaborative and mobile environments by communities of instructors and learners. The FelderSilverman model is followed to tailor courses to the student’s learning style. Self Organizing Maps (SOM) are applied to identify the student’s learning style. The introduction of a social learning network to create, view and manage adaptive intelligent tutoring systems, and a novel method to identify the student’s learning style, are the contributions of this paper.
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Cortés, J. C.; Jódar, L.; Villanueva, R. J.; Villafuerte, L.
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3 Citations
This paper deals with the construction of numerical solution of nonlinear random matrix initial value problems by means of a random Euler scheme. Conditions for the mean square convergence of the method are established avoiding the use of pathwise information. Finally, one includes several illustrative examples where the main statistics properties of the stochastic approximation processes are given.
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FrancoArcega, Anilu; CarrascoOchoa, José Ariel; SánchezDíaz, Guillermo; MartínezTrinidad, José Fco.
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In this paper, we introduce an incremental induction of multivariate decision tree algorithm, called IIMDTS, which allows choosing a different splitting attribute subset in each internal node of the decision tree and it processes large datasets. IIMDTS uses all instances of the training set for building the decision tree without storing the whole training set in memory. Experimental results show that our algorithm is faster than three of the most recent algorithms for building decision trees for large datasets.
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PerezTellez, Fernando; Pinto, David; Cardiff, John; Rosso, Paolo
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1 Citations
In recent years we have seen a vast increase in the volume of information published on weblog sites and also the creation of new web technologies where people discuss actual events. The need for automatic tools to organize this massive amount of information is clear, but the particular characteristics of weblogs such as shortness and overlapping vocabulary make this task difficult. In this work, we present a novel methodology to cluster weblog posts according to the topics discussed therein. This methodology is based on a generative probabilistic model in conjunction with a SelfTerm Expansion methodology. We present our results which demonstrate a considerable improvement over the baseline.
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TenorioGonzalez, Ana C.; Morales, Eduardo F.; VillaseñorPineda, Luis
14 Citations
Reinforcement Learning is commonly used for learning tasks in robotics, however, traditional algorithms can take very long training times. Reward shaping has been recently used to provide domain knowledge with extra rewards to converge faster. The reward shaping functions are normally defined in advance by the user and are static. This paper introduces a dynamic reward shaping approach, in which these extra rewards are not consistently given, can vary with time and may sometimes be contrary to what is needed for achieving a goal. In the experiments, a user provides verbal feedback while a robot is performing a task which is translated into additional rewards. It is shown that we can still guarantee convergence as long as most of the shaping rewards given per state are consistent with the goals and that even with fairly noisy interaction the system can still produce faster convergence times than traditional reinforcement learning techniques.
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RodriguezTello, Eduardo; TorresJimenez, Jose
9 Citations
This paper presents a new Memetic Algorithm (MA) designed to compute nearoptimal solutions for the covering array construction problem. It incorporates several distinguished features including an efficient heuristic to generate a good quality initial population, and a local search operator based on a fine tuned Simulated Annealing (SA) algorithm employing a carefully designed compound neighborhood. Its performance is investigated through extensive experimentation over well known benchmarks and compared with other stateoftheart algorithms, showing improvements on some previous bestknown results.
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Sidorov, Grigori; PichardoLagunas, Obdulia; ChanonaHernandez, Liliana
1 Citations
During years, the community of Mayan researchers was not open to the usage of computer tools. Still, the progress of the computer science and the current state of Mayan research proves the necessity of this type of software. We present the project related to the development of Mayan script database, which is the first necessary step in development of computer representation of Mayan script data. The database contains several tables and allows for various queries. The main idea of the project is the development of the system that would allow managing Mayan script data for specialists and as well for persons without any previous knowledge of Maya. The system includes structural visual description of glyph images and search facilities based on the structural description, along with traditional database search facilities. The user can define any set of visual characteristics. The glyphs are characterized using a “naive” feature set. Fully working prototype is presented. Preliminary evaluation of the efficiency is carried out. In fact, the system can be applied to any set of images that can be assigned structural features.
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Sánchez, Gabriela; Mendoza, Sonia; Decouchant, Dominique; GallardoLópez, Lizbeth; Rodríguez, José
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The development of plastic user interfaces constitutes a promising research topic. They are intentionally designed to automatically adapt themselves to changes of their context of use defined in terms of the user (e.g., identity and role), the environment (e.g., location and available information/tools) and the platform. Some singleuser systems already integrate some plasticity capabilities, but this topic remains quasiunexplored in CSCW. This work is centered on prototyping a plastic collaborative whiteboard that adapts itself: 1) to the platform, as it can be launched from heterogeneous computer devices and 2) to each collaborator, when he is working from several devices. This application can split its interface between the users’ devices in order to facilitate the interaction. Thus, the distributed interface components work in the same way as if they were colocated within a unique device. At any time, group awareness is maintained among collaborators.
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Mesa, Alejandro; FeregrinoUribe, Claudia; Cumplido, René; HernándezPalancar, José
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3 Citations
Mining frequent itemsets in large databases is a widely used technique in Data Mining. Several sequential and parallel algorithms have been developed, although, when dealing with high data volumes, the execution of those algorithms takes more time and resources than expected. Because of this, finding alternatives to speed up the execution time of those algorithms is an active topic of research. Previous attempts of acceleration using custom architectures have been limited because of the nature of the algorithms that have been conceived sequentially and do not exploit the intrinsic parallelism that the hardware provides. The innovation in this paper is a highly parallel algorithm that utilizes a vertical bit vector (VBV) data layout and its feasibility for making support counting. Our results show that for dense databases a custom architecture for this algorithm can perform faster than the fastest architecture reported in previous works by one order of magnitude.
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Muñoz, Karla; Kevitt, Paul Mc; Lunney, Tom; Noguez, Julieta; Neri, Luis
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1 Citations
To ensure learning, gamebased learning environments must incorporate assessment mechanisms, e.g. Intelligent Tutoring Systems (ITSs). ITSs are focused on recognising and influencing the learner’s emotional or motivational states. This research focuses on designing and implementing an affective student model for intelligent gaming, which reasons about the learner’s emotional state from cognitive and motivational variables using observable behaviour. A Probabilistic Relational Models (PRMs) approach is employed to derive Dynamic Bayesian Networks (DBNs). The model uses the ControlValue theory of ‘achievement emotions’ as a basis. A preliminary test was conducted to recognise the students’ prospectiveoutcome emotions with results presented and discussed. PlayPhysics is an emotional games learning environment for teaching Physics. Once the affective student model proves effective it will be incorporated into PlayPhysics’ architecture. The design, evaluation and postevaluation of PlayPhysics are also discussed. Future work will focus on evaluating the affective student model with a larger population of students, and on providing affective feedback.
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Alba, Alfonso; ArceSantana, Edgar; Rivera, Mariano
3 Citations
Motion estimation is one of the most important tasks in computer vision. One popular technique for computing dense motion fields consists in defining a large enough set of candidate motion vectors, and assigning one of such vectors to each pixel, so that a given cost function is minimized. In this work we propose a novel method for finding a small set of adequate candidates, making the minimization process computationally more efficient. Based on this method, we present algorithms for the estimation of dense optical flow using two minimization approaches: one based on a classic blockmatching procedure, and another one based on entropycontrolled quadratic Markov measure fields which allow one to obtain smooth motion fields. Finally, we present the results obtained from the application of these algorithms to examples taken from the Middlebury database.
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Vázquez, Roberto A.; Sossa, Humberto; Garro, Beatriz A.
1 Citations
Visual attention is a powerful mechanism that enables perception to focus on a small subset of the information picked up by our eyes. It is directly related to the accuracy of an object categorization task. In this paper we adopt those biological hypotheses and propose an evolutionary visual attention model applied to the face recognition problem. The model is composed by three levels: the attentive level that determines where to look by means of a retinal ganglion network simulated using a network of bistable neurons and controlled by an evolutionary process; the preprocessing level that analyses and process the information from the retinal ganglion network; and the associative level that uses a neural network to associate the visual stimuli with the face of a particular person. To test the accuracy of the model a benchmark of faces is used.
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SánchezDiaz, Juan Carlos; RamírezCortes, Juan Manuel; EnriquezCaldera, Rogerio; GomezGil, Pilar
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1 Citations
This paper presents an online signature biometric system based on a modified Dynamic Time Warping (DTW) algorithm applied to the signature wavelet coefficients. The modification on DTW relies on the use of direct matching points information (DMP) to dynamically adapt the similarity measure during the matching process, which is shown to increase the verification success rate. The wavelet analysis is done using a subband coding algorithm at global and local level. The use of wavelet coefficients showed a considerable reduction in processing time and an improvement in the equal error recognition rate (EER). The system was tested using a locally constructed database. A comparison of the ROC curves obtained in each case is presented.
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Abundez Barrera, Itzel; Rendón Lara, Eréndira; Gutiérrez Estrada, Citlalih; Díaz Zagal, Sergio
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The possibility of developing systems to support medical diagnosis through Artificial Intelligence (AI) allows conceiving Expert Systems (ES), which constitute successful methods to solve AI problems to program intelligent sys tems. This work deals with creating an ES to support the diagnosis of cervical lesions by identifying them in colposcopic images; for this purpose, 140 images were analyzed, with the most interesting and relevant result from this action being the definition of discriminating features: surface, color, texture and edges. These features will be used to evaluate an image and offer diagnosis as established by the expert physician, like: normal, inflammation process, immature metaplasia, gland eversion and lowgrade lesion. To evaluate the system’s performance, we obtained support from an expert colposcopy physician, who evaluated all 140 images. The results indicated that the ES obtained an efficiency of 75.75 % and an error percentage of 20.405%, including 4.04% that was not evaluated by the expert, who declared that the region or lesion was impossible to identify because the image was not clear.
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BalderasContreras, Tomás; RodriguezGomez, Gustavo; Cumplido, René
Current mobile digital communication systems must implement rigorous operations to guarantee high levels of confidentiality and integrity during transmission of critical information. To achieve higher performance, the security algorithms are usually implemented as dedicated hardware functional units attached to the main processing units of the embedded communication system. To save hardware resources, the designer usually performs a number of manipulations in the cipher algorithm lying at the core of the confidentiality and integrity operations to implement a simplified version of it that is suitable to be efficiently used in an embedded environment. This paper describes an extension to UML 2.0 to model the structure of contemporary block cipher algorithms, with the ultimate goal of synthesizing representations in a hardware description language from these models according to a modeldriven development principle. This automated process should alleviate design complexity and increase the productivity of the developer during experimentation with different design alternatives.
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Gelbukh, Alexander; Sidorov, Grigori; LavinVilla, Eduardo; ChanonaHernandez, Liliana
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9 Citations
In the paper we present a method that allows an extraction of singleword terms for a specific domain. At the next stage these terms can be used as candidates for multiword term extraction. The proposed method is based on comparison with general reference corpus using loglikelihood similarity. We also perform clustering of the extracted terms using kmeans algorithm and cosine similarity measure. We made experiments using texts of the domain of computer science. The obtained term list is analyzed in detail.
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Alejo, R.; Sotoca, J. M.; Valdovinos, R. M.; Toribio, P.
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1 Citations
The quality and size of the training data sets is a critical stage on the ability of the artificial neural networks to generalize the characteristics of the training examples. Several approaches are focused to form training data sets by identification of border examples or core examples with the aim to improve the accuracy of network classification and generalization. However, a refinement of data sets by the elimination of outliers examples may increase the accuracy too. In this paper, we analyze the use of different editing schemes based on nearest neighbor rule on the most popular neural networks architectures.
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OlveraLópez, J. Arturo; CarrascoOchoa, J. Ariel; MartínezTrinidad, J. Francisco; Kittler, Josef
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143 Citations
In supervised learning, a training set providing previously known information is used to classify new instances. Commonly, several instances are stored in the training set but some of them are not useful for classifying therefore it is possible to get acceptable classification rates ignoring non useful cases; this process is known as instance selection. Through instance selection the training set is reduced which allows reducing runtimes in the classification and/or training stages of classifiers. This work is focused on presenting a survey of the main instance selection methods reported in the literature.
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GarcíaBorroto, Milton; MartínezTrinidad, José Francisco; CarrascoOchoa, Jesús Ariel
6 Citations
Obtaining an accurate class prediction of a query object is an important component of supervised classification. However, it could be important to understand the classification in terms of the application domain, mostly if the prediction disagrees with the expected results. Many accurate classifiers are unable to explain their classification results in terms understandable by an application expert. Emerging Pattern classifiers, on the other hand, are accurate and easy to understand. However, they have two characteristics that could degrade their accuracy: global discretization of numerical attributes and high sensitivity to the support threshold value. In this paper, we introduce a novel algorithm to find emerging patterns without global discretization, which uses an accurate estimation of the support threshold. Experimental results show that our classifier attains higher accuracy than other understandable classifiers, while being competitive with Nearest Neighbors and Support Vector Machines classifiers.
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MartínezRuiz, Francisco J.; Vanderdonckt, Jean; Muñoz, Jaime
Zoomable user interfaces are more attractive because they offer the possibility to present information and to support actions according to a "focus+context" method: while a context of use is preserved or presented in a more compact way, the focus can be achieved on some part of the information and actions, enabling the end user to focus on one part at a time. While this interaction technique can be straightforwardly applied for manipulating objects of the same type (e.g., cells in a spreadsheet or appointments in a calendar), it is less obvious how to present interactive tasks of an information system where tasks may involve very different amount and types of information and actions. For this purpose, this paper introduces a metric based on a task model in order to decide what portion of a task model should lead to a particular user interface container, group, or fragment, while preserving the task structure. Each branch of the task model is assigned to a weight that will lead to such a container, group, or fragment depending on parameters computed on variables belonging to the context of use. In this way, not only the task structure is preserved, but also the decomposition of the user interface into elements depends on the context of use, particularly the constraints imposed by the computing platform.
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By
Jimenez, Sergio; Gonzalez, Fabio; Gelbukh, Alexander
5 Citations
The classical set theory provides a method for comparing objects using cardinality and intersection, in combination with wellknown resemblance coefficients such as Dice, Jaccard, and cosine. However, set operations are intrinsically crisp: they do not take into account similarities between elements. We propose a new generalpurpose method for comparison of objects using a soft cardinality function that show that the soft cardinality method is superior via an auxiliary affinity (similarity) measure. Our experiments with 12 text matching datasets suggest that the soft cardinality method is superior to known approximate string comparison methods in text comparison task.
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PragaAlejo, Rolando J.; TorresTreviño, Luis M.; González, David S.; AcevedoDávila, Jorge; Cepeda, Francisco
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Neural Networks (NNs) have been widely used in many industrial processes for prediction and optimization and they have been proven to be useful tools for explaining complex processes. The main objective of this work consists of improving the accuracy of a Radial Basis Function Neural Network Redesigned by Genetic Algorithm and Mahalanobis distance for predicting a welding process. The evaluation function in this approach considers the use of the Coefficient of Determination R^{2}. The results indicated that the statistical method R^{2} is a good alternative to validate the efficiency of the Neural Network model. The principal conclusion in this work is that the Radial Basis Function Redesigned by Genetic Algorithm and Mahalanobis distance had a very good performance in a real case, considering the prediction of specific responses in a welding process.
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By
Chávez, Edgar; Sadit Tellez, Eric
2 Citations
Nearest neighbor queries can be satisfied, in principle, with a greedy algorithm under a proximity graph. Each object in the database is represented by a node, and proximal nodes in this graph will share an edge. To find the nearest neighbor the idea is quite simple, we start in a random node and get iteratively closer to the nearest neighbor following only adjacent edges in the proximity graph. Every reachable node from current vertex is reviewed, and only the closertothequery node is expanded in the next round. The algorithm stops when none of the neighbors of the current node is closer to the query. The number of revised objects will be proportional to the diameter of the graph times the average degree of the nodes. Unfortunately the degree of a proximity graph is unbounded for a general metric space [1], and hence the number of inspected objects can be linear on the size of the database, which is the same as no indexing at all.
In this paper we introduce a quasiproximity graph induced by the allknearest neighbor graph. The degree of the above graph is bounded but we will face local minima when running the above greedy algorithm, which boils down to have false positives in the queries.
We show experimental results for high dimensional spaces. We report a recall greater than 90% for most configurations, which is very good for many proximity searching applications, reviewing just a tiny portion of the database.
The space requirement for the index is linear on the database size, and the construction time is quadratic in worst case. Relaxations of our method are sketched to obtain practical subquadratic implementations.
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GutiérrezRodríguez, Andrés Eduardo; MedinaPérez, Miguel Angel; MartínezTrinidad, José Fco.; CarrascoOchoa, Jesús Ariel; GarcíaBorroto, Milton
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4 Citations
Ultraviolet Spectra (UVS) analysis is a frequent tool in tasks like diseases diagnosis, drugs detection and hyperspectral remote sensing. A key point in these applications is the UVS comparison function. Although there are several UVS comparisons functions, creating good dissimilarity functions is still a challenge because there are different substances with very similar spectra and the same substance may produce different spectra. In this paper, we introduce a new spectral dissimilarity measure for substances identification, based on the way experts visually match the spectra shapes. We also combine the new measure with the Spectral Correlation Measure. A set of experiments conducted with a database of real substances reveals superior results of the combined dissimilarity, with respect to stateoftheart measures. We use Receiver Operating Characteristic curve analysis to show that our proposal get the best tradeoff between false positive rates and true positive rates.
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BarrónEstrada, M. L.; ZatarainCabada, Ramón; ZatarainCabada, Rosalío; BarbosaLeón, Hector; ReyesGarcía, Carlos A.
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Knowledge Societies also named Social Learning Networks (SLN) allow interaction and collaboration between individuals (instructors and students), who share their connections under a scheme of learning communities around common learning interest. In this paper, we present Zamná, a Knowledge Society implemented as an adaptive learning social network. A community of Instructors and Learners can create, display, share and assess communities, intelligent tutoring systems or adaptive courses in a collaborative environment. The communities and courses are tailored to the student’s learning style according to the learning style model of FelderSilverman. The identification of community’s and student’s learning style is performed using selforganizing maps. The main contribution of this paper lies at the integration of Artificial Intelligence with SLN.
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By
McIntosh, Harold V.
1 Citations
A catalog is presented, of those Life forms on strips of widths up to nine whose translational behavior during a single generation can be inferred from two levels of de Bruijn diagrams. The paper is written in October 26, 1988; revised July 20, 1992.
By
Jimenez, J. F.; Cuevas, F. J.; Sossa, J. H.; Gomez, L. E.
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A parametric method to carry out fringe pattern demodulation by means of Differential Evolution is presented. The phase is approximated by the parametric estimation of an nthgrade polynomial so that no further unwrapping is required. On the other hand, a different parametric function can be chosen according to the prior knowledge of the phase behavior. A differential evolution is codified with the parameters of the function that estimates the phase. The differential evolution evolves until a fitness average threshold is obtained. The method can demodulate noisy fringe patterns and even a oneimage closedfringe pattern successfully.
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By
MoyaSánchez, E. Ulises; BayroCorrochano, Eduardo
5 Citations
Atomic Functions are widely used in different applications in image processing, pattern recognition, computational physics and also in the digital interpretation of signal measurements. The main contribution of this work is to develop a Quaternionic Atomic Function Wavelet as a new quaternionic image wavelet transform. This filter have a real part and three imaginary parts (i,j,k) of the Quaternion Atomic Function, as a result we can extract more information from the image by the three phases (φ,θ, ϕ) of the quaternion representation. The experimental part shows clearly that the phase information of the image is not afected by illumination changes.
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By
Arellano, Gerardo; Sucar, Luis Enrique; Morales, Eduardo F.
Automatic image annotation refers to the process of automatically labeling an image with a predefined set of keywords. Image annotation is an important step of contentbased image retrieval (CBIR), which is relevant for many realworld applications. In this paper, a new algorithm based on multiple grid segmentation, entropybased information and a Bayesian classifier, is proposed for an efficient, yet very effective, image annotation process. The proposed approach follows a two step process. In the first step, the algorithm generates grids of different sizes and different overlaps, and each grid is classified with a Naive Bayes classifier. In a second step, we used information based on the predicted class probability, its entropy, and the entropy of the neighbors of each grid element at the same and different resolutions, as input to a second binary classifier that qualifies the initial classification to select the correct segments. This significantly reduces false positives and improves the overall performance. We performed several experiments with images from the MSRC9 database collection, which has manual ground truth segmentation and annotation information. The results show that the proposed approach has a very good performance compared to the initial labeling, and it also improves other scheme based on multiple segmentations.
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By
Cabanillas, Javier; Morales, Eduardo F.; Sucar, Luis Enrique
2 Citations
Searching for an object in an environment using a mobile robot is a challenging task that requires an algorithm to define a set of points in which to sense the environment and an effective traversing strategy, to decide the order in which to visit such points. Previous work on sensing strategies normally assume unrealistic conditions like infinite visibility of the sensors. This paper introduces the concept of recognition area that considers the robot’s perceptual limitations. Three new sensing algorithms using the recognition area are proposed and tested over 20 different maps of increasing difficulty and their advantages over traditional algorithms are demonstrated. For the traversing strategy, a new heuristic is defined that significantly reduces the branching factor of a modified Branch & Bound algorithm, producing paths which are not too far away from the optimal paths but with several orders of magnitude faster that a traditional Branch & Bound algorithm.
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By
ZatarainCabada, Ramón; BarrónEstrada, M. L.; Angulo, Viridiana Ponce; García, Adán José; García, Carlos A. Reyes
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2 Citations
In this paper, we present an intelligent tool implemented as a learning social network. An author can create, display, and share lessons, intelligent tutoring systems and other components among communities of learners in webbased and mobile environments. The tutoring systems are tailored to the student’s learning style according to the model of FelderSilverman. The identification of the student’s learning style is performed using selforganizing maps. The main contribution of this paper is the implementation of a learning social network to create, view and manage adaptive and intelligent tutoring systems using a new method for automatic identification of the student’s learning style. We present the architecture of the social network, the method for identifying learning styles, and some experiments made to the social network.
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By
Carrillo, Maya; VillatoroTello, Esaú; LópezLópez, Aurelio; Eliasmith, Chris; VillaseñorPineda, Luis; MontesyGómez, Manuel
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Geographic Information Retrieval (GIR) is a specialized Information Retrieval (IR) branch that deals with information related to geographical locations. Traditional IR engines are perfectly able to retrieve the majority of the relevant documents for most geographical queries, but they have severe difficulties generating a pertinent ranking of the retrieved results, which leads to poor performance. A key reason for this ranking problem has been a lack of information. Therefore, previous GIR research has tried to fill this gap using robust geographical resources (i.e. a geographical ontology), while other research with the same aim has used relevant feedback techniques instead. This paper explores the use of Bag of Concepts (BoC; a representation where documents are considered as the union of the meanings of its terms) and Holographic Reduced Representation (HRR; a novel representation for textual structure) as reranking mechanisms for GIR. Our results reveal an improvement in mean average precision (MAP) when compared to the traditional vector space model, even if Pseudo Relevance Feedback is employed.
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By
Valdez, Fevrier; Melin, Patricia; Castillo, Oscar
5 Citations
We describe in this paper an approach for mathematical function optimization using fuzzy logic for parameter tuning combining Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs). The proposed method combines the advantages of PSO and GA to give us an improved FPSO+FGA hybrid method. Fuzzy logic is helpful to find the optimal parameters in PSO and GA in the best way possible. Also, with the tuning of parameters based on fuzzy logic it is possible to balance the exploration and exploitation of the proposed method. The hybrid method is called FPSO+FGA and was tested with a set of benchmark mathematical functions.
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By
Ríos Gaona, Miguel Angel; Gelbukh, Alexander; Bandyopadhyay, Sivaji
1 Citations
We present our experiments on Recognizing Textual Entailment based on modeling the entailment relation as a classification problem. As features used to classify the entailment pairs we use a symmetric similarity measure and a nonsymmetric similarity measure. Our system achieved an accuracy of 66% on the RTE3 development dataset (with 10fold cross validation) and accuracy of 63% on the RTE3 test dataset.
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By
Zulfiqar, Ali; Muhammad, Aslam; MartinezEnriquez, Ana Maria; EscaladaImaz, G.
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4 Citations
Every feature extraction and modeling technique of voice/speech is not suitable in all type of environments. In many real life applications, it is not possible to use all type of feature extraction and modeling techniques to design a single classifier for speaker identification tasks because it will make the system complex. So instead of exploring more techniques or making the system complex it is more reasonable to develop the classifier by using existing techniques and then combine them by using different combination techniques to enhance the performance of the system. Thus, this paper describes the design and implementation of a VQHMM based Multiple Classifier System by using different combination techniques. The results show that the developed system by using confusion matrix significantly improve the identification rate.
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EspinosaCuriel, Ismael Edrein; RodríguezJacobo, Josefina; FernándezZepeda, José Alberto
1 Citations
In this paper, we propose a set of diagrams to visualize software process reference models (PRM). The diagrams, called dimods, are the combination of some visual and process modeling techniques such as rich pictures, mind maps, IDEF and RAD diagrams. We show the use of this technique by designing a set of dimods for the Mexican Software Industry Process Model (MoProSoft). Additionally, we perform an evaluation of the usefulness of dimods. The result of the evaluation shows that dimods may be a support tool that facilitates the understanding, memorization, and learning of software PRMs in both, software development organizations and universities. The results also show that dimods may have advantages over the traditional description methods for these types of models.
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By
Martínez, Genaro J.; Adamatzky, Andrew; McIntosh, Harold V.
4 Citations
We study a twodimensional cellular automaton (CA), called Diffusion Rule, which exhibits diffusionlike dynamics of propagating patterns. In computational experiments we discover a wide range of mobile and stationary localizations (gliders, oscillators, glider guns, puffer trains), analyze spatiotemporal dynamics of collisions between gliders, and discuss possible applications in unconventional computing.
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By
Guillén, Carlos; Ita, Guillermo; LópezLópez, Aurelio
We research on the possible orientations patterns of a grid graph G, and propose a method for counting certain combinatorial structures over the class of orientations of G. For example, our method can be applied for counting sinkfree orientations of G, as well as it can be applied for solving the #2SAT problem for grid Boolean formulas.
Our proposal extends the classical transfer matrix method used for counting the number of independent sets in a grid.
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By
Meza, Ivan V.; Salinas, Lisset; Venegas, Esther; Castellanos, Hayde; Chavarría, Alejandra; Pineda, Luis A.
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2 Citations
In this paper, we present a new specification, implementation and evaluation of the “guess the card” system. This is a conversational system with spoken Spanish and vision capabilities that plays a game with members of the general public in a permanent stand at a science museum. The system has been built using a methodology and programming environment based on the notion of dialogue model specification and interpretation, that we have been developing over the years. The present system uses the latest version of the formalism and improves considerably over previous versions [8]. In particular, the present version takes advantage of the discourse structure, increases modularity, and incorporates general and flexible recovery strategies. An evaluation of the system is also presented.
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By
Meléndez, Augusto; Sucar, Luis Enrique; Morales, Eduardo F.
2 Citations
Several methods have been developed for face detection with certain success, however these tend to fail under difficult conditions such as partial occlusions and changes in orientation and illumination. We propose a novel technique for face detection based on a visual grammar. We define a symbol relational grammar for faces, representing the visual elements of a face and their spatial relations. This grammar is transformed into a Bayesian network (BN) structure and its parameters are obtained from data, i.e., positive and negative examples of faces. Then the BN is used for face detection via probabilistic inference, using as evidence a set of weak detectors for different face components. We evaluated our method on a set of sample images of faces under difficult conditions, and contrasted it with a simplified model without relations, and the AdaBoost face detector. The results show a significant improvement when using our method.
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By
Reyes, Laura Cruz; Zezzatti, Carlos Alberto Ochoa Ortíz; Santillán, Claudia Gómez; Hernández, Paula Hernández; Fuerte, Mercedes Villa
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8 Citations
In the last years the population of Leon City, located in the state of Guanajuato in Mexico, has been considerably increasing, causing the inhabitants to waste most of their time with public transportation. As a consequence of the demographic growth and traffic bottleneck, users deal with the daily problem of optimizing their travel so that to get to their destination on time. To give a solution to this problem of obtaining an optimized route between two points in a public transportation, a method based on the cultural algorithms technique is proposed. Cultural algorithms are used in the generated knowledge in a set of time periods for a same population, using a belief space. These types of algorithms are a recent creation. The proposed method seeks a path that minimizes the time of traveling and the number of transfers. The results of the experiment show that the technique of the cultural algorithms is applicable to these kinds of multiobjective problems.
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By
Ibargüengoytia, Pablo H.; Delgadillo, Miguel Angel
1 Citations
Fuel oil viscosity is an important parameter in the control of combustion in power plants. If the viscosity is optimal at the entrance of the boiler, then the combustion is optimal causing a minimum of contamination and a maximum of efficiency. Hardware viscosimeters are expensive and difficult to operate. Laboratory analyses calculate the viscosity based on chemical analysis but not in real time. This paper describes the development of a virtual sensor that utilizes artificial intelligence (AI) techniques for the construction of models. The models are used to estimate the viscosity based on related measurements concerning the combustion in a power plant. A probabilistic model is constructed using automatic learning algorithms and an analytical model is defined using physical principles and chemical analysis. Sensor fusion is applied to estimate the online value of the fuel viscosity. The virtual sensor is being installed in the Tuxpan power plant in Veracruz, Mexico.
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CuevasVicenttín, Víctor; VargasSolar, Genoveva; Collet, Christine; Ibrahim, Noha; Bobineau, Christophe
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2 Citations
This paper presents an approach and an associated system named Hypatia for accessing and processing data by coordinating services in dynamic environments. A dynamic environment consists of applications, servers and devices that can be static and nomad, and that produce or consume data on demand (e.g., online applications, Webhosted DBMS) or continuously (messaging systems, mobile services). In such an environment, data are hidden behind services that export application programming interfaces (API) through heterogeneous networks and that provide functions for retrieving and processing data. In order to have an aggregated and integrated view of the dynamic environment at every moment (e.g., accessing Google’s agenda service and feeding a Twitter service for continuously locating friends as we all stroll in a city), data consumers have to execute sets of service calls, i.e., subscribe to continuous data producers, aggregate results and feed other services and then obtain results and eventually start over again. No offtheshelf DBMS provides such service oriented querying approach including continuous, oneshot, mobile and static query evaluation.
Our work introduces the notion of hybrid query that declaratively expresses data consumers requirements and an associated query evaluator, Hypatia, that executes data oriented query service coordinations; taking advantage of the services available in the network (data providers and devices computing capacity) and yielding the query result.
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By
RiveraRovelo, Jorge; BayroCorrochano, Eduardo; Dillmann, Ruediger
1 Citations
In this work we present an algorithm to approximate the surface of 2D or 3D objects combining concepts from geometric algebra and artificial neural networks. Our approach is based on the selforganized neural network called Growing Neural Gas (GNG), incorporating versors of the geometric algebra in its neural units; such versors are the transformations that will be determined during the training stage and then applied to a point to approximate the surface of the object. We also incorporate the information given by the generalized gradient vector flow to select automatically the input patterns, and also in the learning stage in order to improve the performance of the net. Several examples using medical images are presented, as well as images of automatic visual inspection. We compared the results obtained using snakes against the GSOM incorporating the gradient information and using versors. Such results confirm that our approach is very promising. As a second application, a kind of morphing or registration procedure is shown; namely the algorithm can be used when transforming one model at time t_{1} into another at time t_{2}. We include also examples applying the same procedure, now extended to models based on spheres.
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By
Pakray, Partha; Gelbukh, Alexander; Bandyopadhyay, Sivaji
3 Citations
The development of a syntactic textual entailment system that compares the dependency relations in both the text and the hypothesis has been reported. The Stanford Dependency Parser has been run on the 2way RTE3 development set and the dependency relations obtained for a text and hypothesis pair has been compared. Some of the important comparisons are: subjectsubject comparison, subjectverb comparison, objectverb comparison and cross subjectverb comparison. Corresponding verbs are further compared using the WordNet. Each of the matches is assigned some weight learnt from the development corpus. A threshold has been set on the fraction of matching hypothesis relations based on the development set. The threshold score has been applied on the RTE4 gold standard test set using the same methods of dependency parsing followed by comparisons. Evaluation scores obtained on the test set show 54.75% precision and 53% recall for YES decisions and 54.45% precision and 56.2% recall for NO decisions.
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By
Gomez, Roberto; Rojas, Julio Cesar; Mata, Erika
The systems that are able to detect suspicious or malicious activities are a fundamental component in the security process of every organization. These systems generate alerts that correspond to individual events and, in general, these systems do not show the relationships between them. It is important to examine the security data within their overall context in order to better understand what is happening in our systems. In this work, we present a correlation model based on the concept of vector clocks. We also present a tool that is our implementation of this correlation mechanism. This tool can be used by security analysts to generate graphs showing the relationships between the reported events and possibly discovering unknown attack patterns.
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By
CastroMartínez, Francisco J.; Castelán, Mario; LópezJuárez, Ismael
We explore the automatic adjustment of an artificial light source intensity for the purposes of imagebased feature extraction and recognition. Two histogrambased criteria are proposed to achieve this adjustment: a twoclass separation measure for 2D features and a Gaussian distribution measure for 2.5D features. To this end, the light source intensity is varied within a fixed interval as a camera captures one image for each intensity variation. The image that best satisfies the criteria for feature extraction is tested on a neuralnetwork based recognition system. The network considers information related to both 2D (contour) and 2.5D shape (local surface curvature) of different objects. Experimental tests performed during different times of the day confirm that the proposed adjustment delivers improved feature extraction, extending the recognition capabilities of the system and adding robustness against changes in ambient light.
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