Journal of Intelligent & Fuzzy Systems - Volume 34, issue 5

Authors: Muñoz, Julio | Molero-Castillo, Guillermo | Benítez-Guerrero, Edgard | Bárcenas, Everardo

Article Type: Research Article

Abstract: Nowadays, context-aware systems use data obtained from various sources to adapt and provide services of interest to users according to their needs, location or interaction with the corresponding environment. However, the use of heterogeneous sources creates a huge amount of data that may differ in format, transmission speed and may be affected by environmental noise. This generates some inconsistency in data, which must be detected in time to avoid erroneous analysis. This is done using data fusion, which is the action for integrating diverse sources to be analyzed according to a given context. In this work, we propose a scheme …of data fusion of heterogeneous sources, supported by a distributed architecture and Bayesian inference as fusion method. As a practical experiment, data were collected from three DHT22 sensors, whose measurements were relative humidity and temperature. The purpose of the experiment was to analyze the variation of these measurements over 24 hours, and fusion them to obtain integrated data. This proposed of data fusion represents an important field of action for the knowledge generation of interest in context-aware systems, for example for the analysis of the environment in order to take advantage of the use of energy and provide a comfortable working environment for the users. Show more

Keywords: Bayesian Inference, context-aware systems, data fusion, data inconsistency, knowledge generation

DOI: 10.3233/JIFS-169500

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 5, pp. 3165-3176, 2018

Authors: Ramírez-Noriega, Alan | Juárez-Ramírez, Reyes | Jiménez, Samantha | Martínez-Ramírez, Yobani | Figueroa Pérez, J. Francisco

Article Type: Research Article

Abstract: Course sequencing plays a major role in Intelligent Tutoring Systems because it determines the learning path of the student. However, it is difficult to define this order during early stages when there is no interaction with the student. The objective of this study is to determine the sequence of learning concepts considering an ontology and Wikipedia information. We used a text mining algorithm using Wikipedia to determine course sequencing. The knowledge base is formed by concepts and relationships in an ontology, in addition to Wikipedia articles of the same concepts. To evaluate the accuracy of the algorithm, we made a …comparison against domain experts. According to the Pearson test, a correlation of 0.664 between the algorithm and experts was obtained, with a confidence level higher than 99%. The learning sequence can be defined with this method when we do not have evidence of student knowledge, to be later modified according to the interaction of the student. Show more

Keywords: Intelligent tutoring system, ontology, course sequencing, Wikipedia, teaching

DOI: 10.3233/JIFS-169501

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 5, pp. 3177-3185, 2018

Authors: Moo-Mena, Francisco | Hernández-Ucán, Rafael | Ríos-Martínez, Jorge | Gómez-Montalvo, Jorge

Article Type: Research Article

Abstract: The applications based on Web services (WS) composition are gaining momentum as an approach to build autonomous, heterogenous and distributed applications. Often a unique WS does not provide the required funcionality, therefore it is necessary to carry out a composition of WS in order to get the expected results. Optimizing the WS composition requires an efficient way to select the best Web services. Solving this problem can be a very complex task involving many criteria. In this work an approach is proposed to accomplish the WS selection from services previously registered and classified according to their quality of service (QoS) …parameters. QoS parameters are characterized by an ontology and their values stored in a semantic database. The values of QoS corresponding to providers are collected automatically by an evaluation agent of QoS. In this way the service composition benefits from this proposal being easier and more efficient, taking into account only the best services and respecting the quality of service requirements established by the user. The results from the experiments performed with data involving real WS and artificially created data show that the proposed method is a feasible option to do a better selection of WS in the context of a WS composition. Show more

Keywords: Web services, ontologies, quality of service, selection of Web services, composition of Web services

DOI: 10.3233/JIFS-169502

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 5, pp. 3187-3197, 2018

Authors: Pozos-Parra, Pilar | Chávez-Bosquez, Oscar | McAreavey, Kevin

Article Type: Research Article

Abstract: Belief merging aims to combine pieces of information from different (and possibly conflicting) sources so as to produce a single consistent belief base which retains as much information as possible. In this paper, we describe Merginator, a logic-based tool implementing three belief merging operators presented with specific characteristics that make them interesting for comparison: ΔΣ , ΔGMax and Δps . We describe these operators while solving a set of basic examples found in the literature that we translate into natural language to provide insights for Merginator users. We also propose two more complex consensus-seeking examples: an adaptation …to the role play “Lost at Sea” and the subject of administrative response to air pollution in Hong Kong. Results show that all three operators provide a consensus to the scenarios, and Δps gives the most refined consensus. This demonstrates that belief merging, is a viable technique to support consensus decision-making in many domains. Merginator is open source software available at GitHub. Show more

Keywords: Consensus decision-making, knowledge representation, propositional logic, logic-based merging, belief merging operators

DOI: 10.3233/JIFS-169503

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 5, pp. 3199-3210, 2018

Authors: Munk, Michal | Munkova, Dasa

Article Type: Research Article

Abstract: Errors and residuals are closely related measures of the deviation. An error is a deviation of the observed value (PEMT output) from the expected value (MT output), while the residual of the observed value is the difference between the observed and predicted value of quality. We propose an exploratory data technique representing an ideal instrument to evaluate and improve machine translation (MT) systems. The main contribution consists of a rigorous technique (a statistical method), novel to the research of MT evaluation given by residual analysis to identify differences between MT output and post-edited machine translation output regarding human translation (reference). …The residual analysis of the automatic metrics can help us to discover significant differences between MT and PEMT and to identify questionable issues regarding the one reference. In this study, we show the usage of residuals in MT evaluation. Using residual analysis, we identified sentences, in which significant differences were found in the scores of automatic metrics between MT output and post-edited (PE) MT output from Slovak into English. Show more

Keywords: Machine translation, evaluation, residuals, analytical language, inflectional language, MT errors

DOI: 10.3233/JIFS-169504

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 5, pp. 3211-3223, 2018

Authors: Munk, Michal | Munkova, Dasa | Benko, Lubomir

Article Type: Research Article

Abstract: The study describes an experiment with different estimations of reliability. Reliability reflects the technical quality of the measurement procedure such as an automatic evaluation of Machine Translation (MT). Reliability is an indicator of accuracy, the reliability of measuring, in our case, measuring the accuracy and error rate of MT output based on automatic metrics (precision, recall, f-measure, Bleu-n, WER, PER, and CDER). The experiment showed metrics (Bleu-4 and WER) that reduce the overall reliability of the automatic evaluation of accuracy and error rate using entropy. Based on the results we can say, that the use of entropy for the estimation …of reliability brings more accurate results than conventional estimations of reliability (Cronbach’s alpha and correlation). MT evaluation, based on n-grams or edit distance, using entropy could offer a new view on lexicon-based metrics in comparison to commonly used ones. Show more

Keywords: Entropy, machine translation, reliability estimation, quality, automatic MT evaluation

DOI: 10.3233/JIFS-169505

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 5, pp. 3225-3233, 2018

Authors: Fors-Isalguez, Yanet | Hermosillo-Valadez, Jorge | Montes-y-Gómez, Manuel

Article Type: Research Article

Abstract: Automatic text summarization systems are nowadays of great help to extract relevant information from large corpora. Many solutions to the task have been proposed from the perspective of the optimization of a single-objective function, aiming at finding the global optimum. This is an unrealistic goal since when multiple objectives are considered a solution that optimizes one of the objectives may induce the opposite effect on the others. Recently other solutions have been proposed that involve multiple, conflicting objectives, but which eventually are aggregated into a scalar function thus resulting in a single-objective optimization problem. Furthermore, oftentimes a typical bag of …words model is used and little effort has been made to include semantic relations between sentences to improve performance. In this paper a novel method for query-oriented summarization is proposed as a multiobjective optimization problem taking into account the Pareto front and based on an embedded representation of sentences. The method is evaluated with the TAC 2009 dataset. Experimental results show that the approach contributes to improve performance significantly. To the authors’ knowledge, the method is the first attempt to include embedded representations of sentences in a multiobjective optimization solution, which applies the Pareto approach to query-oriented summarization. Show more

Keywords: Query-oriented multi-document summarization, multiobjective-optimization, sentence embedded representation

DOI: 10.3233/JIFS-169506

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 5, pp. 3235-3244, 2018

Authors: Calvo, Hiram | Carrillo-Mendoza, Pabel | Gelbukh, Alexander

Article Type: Research Article

Abstract: In this paper we study how the presence or absence of redundancy on multiple related texts can be used to compute sentence relevance for extractive multi-document summarization. Two types of redundancy can be found: intra-document and inter-document. By experimenting with them, different ideas can be extracted, for example: statements redundant between documents—which can be important by their popularity; statements that are not redundant—which can be important by their novelty; or statements redundant within each document—which can be important by being constantly addressed by a single author. We propose an unsupervised graph-based method that allows to generate summaries based on different …strategies of redundancy. We present experiments on two DUC corpora of nine different strategies to extract information depending of how redundancy within a document and in different documents is managed. According to DUC gold standards, we found that a multi-document generic summary should contain the most redundant (popular) information between different sources while avoiding local intra-document redundancy. We implemented a mechanism to enrich sentence rankings with redundancy, improving the evaluation of summaries. Show more

Keywords: Multi-document summarization, similarity graphs, unsupervised summarization, sentence redundancy, doc2vec

DOI: 10.3233/JIFS-169507

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 5, pp. 3245-3255, 2018

Authors: Al-Marri, Mubarak | Raafat, Hazem | Abdallah, Mustafa | Abdou, Sherif | Rashwan, Mohsen

Article Type: Research Article

Abstract: This paper presents a system for improving the quality of pronunciation error detection and correction for Qur’an recitation by Non-Arabic speakers. Most of the classical speech recognition systems are built using the Hidden Markov Model (HMM) with a Mixture of Gaussian Model (GMM). This paper attempts to enhance the GMM-HMM model’s performance by using Deep Neural Networks (DNNs). The major part of the work done in this paper is involved in the collection and processing of speakers’ data, and building and evaluation of baseline GMM system and the proposed DNN acoustic models for the Qur’an recitation framework. With the aim …of solving some pronunciation problems and enhancing the overall performance of such a speech recognition system, we replace the mixture of Gaussians with a DNN. The DNN-HMM model outperforms the GMM-HMM model by 1.02% based on HTK’s word accuracy equation. By calculating the insertion results for both models, DNN-HMM showed progress by 2.59%. In addition, in substitution results, DNN-HMM shows progress with the confusion phonemes DAA by 15.09% and DHA by 17.28%. All experiments and results are presented and discussed in detail. Show more

Keywords: Computer Aided Language Pronunciation, Hidden Markov Model, Automatic Speech Recognition, Deep Neural Network

DOI: 10.3233/JIFS-169508

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 5, pp. 3257-3271, 2018

Authors: Pérez-Espinosa, Humberto | Reyes-Meza, Verónica | Aguilar-Benitez, Emanuel | Sanzón-Rosas, Yuvila M.

Article Type: Research Article

Abstract: The bark is a very distinctive vocalization of the dog. It is very common and a mean for interaction with humans. However, the scope of their automated analysis by computational techniques, as well as the possible applications to which they can give rise have been little explored. In this study, we describe the process to develop an automatic classifier that can identify individual dogs based on their barks. We created a database with more than 6,000 barks applying positive and negative stimuli to dogs. We acoustically characterized the barking samples using a signal processing tool that extracts large sets of …features. Based on these sets, we generated optimal subsets of features to feed machine learning algorithms which trained classification models. We evaluated such models and compared the classification performance of different algorithms. We analyzed the pertinence of training specific models per each breed. The classification obtained outperform the results previously reported in similar works. Our findings suggest that practical applications could be constructed on this kind of technology. Show more

Keywords: Machine learning, domestic dog barking, acoustic analysis

DOI: 10.3233/JIFS-169509

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 5, pp. 3273-3280, 2018