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

Authors: Sánchez, Belém Priego | Pinto, David | Singh, Vivek Kumar

Article Type: Research Article

Abstract: A Verbal Phraseological Unit (VPU) is a phrase or expression that has either a literal or figurative (non-compositional) meaning. Identifying, in an automatic manner, which meaning is associated to a VPU in a certain context is an open problem in natural language processing, whose solution impacts in various real life applications. In this paper we present a method for the automatic verification of non-compositionality of a VPU based on the use of lexical domains. The methodology proposed is based in the degree of overlapping between the VPU domain and its context domain. This methdology is general in the sense that …it opens the opportunity of applying different overlapping metrics, and different methods for calculating the domain of a given phrase. The experiments carried out show that this novel approach can be efficiently applied in high volumes of data with competitive results close to 65%. Show more

Keywords: Idiom, disambiguation, lexical domain

DOI: 10.3233/JIFS-169490

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 5, pp. 3059-3067, 2018

Authors: Vicente, Marta | Barros, Cristina | Lloret, Elena

Article Type: Research Article

Abstract: This paper proposes an end-to-end Natural Language Generation approach to automatically create fiction stories using statistical language models. The proposed approach integrates the stages of macroplanning and the surface realisation, necessary to determine the content to write about together with the structure of the story, and the syntactic and lexical realisation of sentences to be generated, respectively. Moreover, the use of language models within the stages allows the generation task to be more flexible, as far as the adaptation of the approach to different languages, domains and textual genres is concerned. In order to validate our approach, two evaluations were …performed. On the one hand, the influence of integrating position-specific language modelling in the macroplanning stage into the surface realisation module was evaluated. On the other hand, a user evaluation was performed to analyse the generated stories in a qualitative manner. Although there is still room for improvement, the results obtained from the first evaluation in conjunction with the user evaluation feedback shows that the combination of the aforementioned stages in an end-to-end approach is appropriate and have positive effects in the resulting generated text. Show more

Keywords: Natural language generation, language modelling, document planning, surface realisation, automatic story generation

DOI: 10.3233/JIFS-169491

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 5, pp. 3069-3079, 2018

Authors: Mager, Manuel | Carrillo, Diónico | Meza, Ivan

Article Type: Research Article

Abstract: In this work, we present a morphological segmenter for the Mexican indigenous language Wixarika. Segmentation is fundamental for rich morphological languages, a common aspect of the native American languages, to improve other tasks like machine translation, dialogue systems, summarization, etc. On top of the agglutinative nature of the language, the low amount of resources and the lack of an orthographic standard among dialects add to the challenge. Our proposal is based on a probabilistic finite-state approach that exploits regular agglutinative patterns and requires little linguistic knowledge. We show that our approach outperforms unsupervised and semi-supervised methods in a low-resource context. …The dataset used in this work was openly released for future work by the community. Show more

Keywords: Morphology, low resources, finite-state transducer, Wixarika, endangered languages

DOI: 10.3233/JIFS-169492

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 5, pp. 3081-3087, 2018

Authors: Ayala-Gómez, Frederick | Daróczy, Bálint | Benczúr, András | Mathioudakis, Michael | Gionis, Aristides

Article Type: Research Article

Abstract: Scholarly search engines, reference management tools, and academic social networks enable modern researchers to organize their scientific libraries. Moreover, they often provide recommendations for scientific publications that might be of interest to researchers. Because of the exponentially increasing volume of publications, effective citation recommendation is of great importance to researchers, as it reduces the time and effort spent on retrieving, understanding, and selecting research papers. In this context, we address the problem of citation recommendation , i.e., the task of recommending citations for a new paper. Current research investigates this task in different settings, including cases where rich user metadata …is available (e.g., user profile, publications, citations). This work focus on a setting where the user provides only the abstract of a new paper as input. Our proposed approach is to expand the semantic features of the given abstract using knowledge graphs – and, combine them with other features (e.g., indegree, recency) to fit a learning to rank model. This model is used to generate the citation recommendations. By evaluating on real data, we show that the expanded semantic features lead to improving the quality of the recommendations measured by nDCG@10. Show more

Keywords: Citation recommendations, knowledge graphs, recommender systems

DOI: 10.3233/JIFS-169493

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 5, pp. 3089-3100, 2018

Authors: Piryani, Rajesh | Gupta, Vedika | Singh, Vivek Kumar | Pinto, David

Article Type: Research Article

Abstract: Books are an important source of knowledge to disseminate information. Researchers and academicians write books to propagate their innovative research or teachings amongst academic as well as non-academic audience. The number of books written every year is increasing rapidly. According to International Publisher Association (IPA) annual report 2015–2016, around 150 million different books were published worldwide in 2014–2015. Many e-commerce websites are also involved in selling books. A recent addition to book publishing world is e-books, which have really made it very simple to publish. While, availability of large number of books is good for readers, at the same time …it is challenging to find a good book, particularly in scholarly settings. Researchers in the area of Scientometrics have attempted to view assessment of goodness of a scholarly book by measuring citations that a book receive. However, citations alone are not a true measure of a book’s impact. Many a times people use the knowledge in a book without actually citing it. Also use of books in classroom settings or for general reading often is not reflected in terms of citations. Therefore, it is important to obtain users’s opinion about a book from other forms of data. Fortunately, we have now some data of this sort available in form of reviews, downloads and social media mentions etc. Amazon and Goodreads, both of which provide the readers’ views about a book, are two good examples. This paper presents an exploratory research work on using these non-traditional data about books to assess impact of a book. A set of Scopus-indexed computer science books with good citations as well as some other popular books in computer science domain are used for analysis. The reviews of books have been crawled in an automated fashion from Amazon and Goodreads. Thereafter sentiment analysis is carried out the text of reviews. Results of sentiment analysis are compared and correlated with traditional impact assessment metrics. The experimental analysis does not show a coherent relationship between citation and online reviews. Also, majority of the online reviews are found to be positive for large number of books in the dataset. As a related exercise, the Scopus citation data and Google scholar citation data for books are also compared. A high value of correlation is observed in these two. Overall the exploratory analysis provides a useful insight into the problem of book impact assessment. Show more

Keywords: Altmetrics, book impact, citation impact, review mining, sentiment analysis

DOI: 10.3233/JIFS-169494

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 5, pp. 3101-3110, 2018

Authors: Banshal, Sumit Kumar | Singh, Vivek Kumar | Kaderye, Golam | Muhuri, Pranab Kumar | Sánchez, Belém Priego

Article Type: Research Article

Abstract: Scholarly articles are considered one of the primary medium for dissemination of inventions and discoveries. Traditionally, usefulness and popularity of a scholarly article has been measured in terms of citations it receives. However, in the changed research publishing landscape, where most of the publications are now available in digital form accessible through various digital libraries; new measures of measuring usefulness of scholarly articles have emerged. Nowadays, scholarly articles are easily available for access and download from various digital access portals. The use and popularity of these digital access portals has also made it possible to integrate various social media platforms …with journal access and use. Most of the journals now maintain statistics about reads, number of downloads, social profile shares etc. Several newer platforms like ResearchGate, Academia and Mendeley have also become popular. Researchers now often share their articles on various such platforms and also use social media channels to disseminate their article to a wider audience. This transformed environment has allowed to track and measure usefulness and popularity of scholarly articles through alternative metrics (now popularly known as Altmetrics) as compared to traditional citation impact measures. Altmetrics attempts to derive impact of a scholarly article by using data from different kinds such as social network share, mentions, tweets etc. The use of Altmetrics varies widely from country to country and discipline to discipline. This paper attempts to present findings of an exploratory analysis of relevance of Altmetrics data through a case study of scholarly articles from India published during 2016 and indexed in Web of Science and also updated on ResearchGate. The results obtained provide an interesting insight on relatedness and correlation of presence of scholarly articles in Web of Science and ResearchGate. It is observed that about 61% papers indexed in Web of Science have an entry in ResearchGate. There are, however, disciplinary variations in presence of articles in ResearchGate. Only about 61% of the total disciplines in Web of Science are found to be covered in ResearchGate. Show more

Keywords: Scholarly articles, altmetrics, ResearchGate, social network analysis, Indian Research Output

DOI: 10.3233/JIFS-169495

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 5, pp. 3111-3118, 2018

Authors: García-Ramírez, Jesús | Olvera-López, J. Arturo | Olmos-Pineda, Ivan | Martín-Ortíz, Manuel

Article Type: Research Article

Abstract: Facial Expression Recognition (FER) is a research area that has been interesting for computer science community in recent years. In this paper, we propose a methodology for the three stages of a FER system. In the pre-processing stage a method based on edge detectors and thresholding operators for eyebrow and mouth segmentation is proposed; the next stage is feature extraction, we propose using polynomials as features for describing eyebrows and mouth regions. Finally, in classification stage different supervised learners such as: Neural Networks, K-Nearest Neighbors and C4.5 decision trees are tested in order to obtain a model for classifying three …out of six basic emotions (anger, happiness and surprise). According to our results, the proposed approach has acceptable accuracy for predicting new examples. Show more

Keywords: Expression recognition, face images pre-processing, supervised classification, interpolation features

DOI: 10.3233/JIFS-169496

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 5, pp. 3119-3131, 2018

Authors: Álvarez-Carmona, Miguel A. | Pellegrin, Luis | Montes-y-Gómez, Manuel | Sánchez-Vega, Fernando | Escalante, Hugo Jair | López-Monroy, A. Pastor | Villaseñor-Pineda, Luis | Villatoro-Tello, Esaú

Article Type: Research Article

Abstract: The goal of Author Profiling (AP) is to identify demographic aspects (e.g., age, gender) from a given set of authors by analyzing their written texts. Recently, the AP task has gained interest in many problems related to computer forensics, psychology, marketing, but specially in those related with social media exploitation. As known, social media data is shared through a wide range of modalities (e.g., text, images and audio), representing valuable information to be exploited for extracting valuable insights from users. Nevertheless, most of the current work in AP using social media data has been devoted to analyze textual information only, …and there are very few works that have started exploring the gender identification using visual information. Contrastingly, this paper focuses in exploiting the visual modality to perform both age and gender identification in social media, specifically in Twitter. Our goal is to evaluate the pertinence of using visual information in solving the AP task. Accordingly, we have extended the Twitter corpus from PAN 2014, incorporating posted images from all the users, making a distinction between tweeted and retweeted images. Performed experiments provide interesting evidence on the usefulness of visual information in comparison with traditional textual representations for the AP task. Show more

Keywords: Visual author profiling, age identification, gender identification, social media, Twitter, CNN representation

DOI: 10.3233/JIFS-169497

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 5, pp. 3133-3145, 2018

Authors: Lara-Álvarez, Carlos | Reyes, Tania | Rodríguez-Rangel, Hector

Article Type: Research Article

Abstract: Counting the number of words and lines that a user reads is important for many educational purposes – e.g., the reading speed is a key factor to improve learning, intelligent systems can suggest text that must be read to achieve a determined learning objective. The eye tracking technology is commonly used to analyze the user reading habits. Counting the number of read words could be hard when the readings are obtained from imprecise eye tracking data – e.g., eye tracking calibration difficulties. Approaches that find patterns from saccades and fixations usually fail to solve the problem in such conditions. This …paper introduces the Cowl approach, which deals with the imprecision problem by associating the eye tracking data with points obtained from character recognition. To detect text lines truly read, the problem is stated as one of merging two hypothetical lines and it is solved by a Bayesian approach. Tests show that the proposed approach shows high performance, reaching average precision rates up to 0.866 for recall 0.976 – in the case of text with different orientations. Show more

Keywords: Eye tracking, multiple lines fitting, human computer interaction, line features

DOI: 10.3233/JIFS-169498

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 5, pp. 3147-3154, 2018

Authors: De Ita, Guillermo | Marcial-Romero, Raymundo | Bello, Pedro | Contreras, Meliza

Article Type: Research Article

Abstract: Let K be a knowledge base (KB) and let φ be new information, both propositional formulas expressed in conjunctive form (CF). We propose a deterministic and correct algorithm for performing the belief revision of φ in K , denoted as: K  ∘ φ . Our proposal satisfies subsets of AGM and KM postulates. We also present the soundness proof of our belief revision method, and the analysis of its time complexity.

Keywords: Belief revision, propositional inference, AGM postulates, KM postulates

DOI: 10.3233/JIFS-169499

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 5, pp. 3155-3164, 2018

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

Authors: Peralta-Malváez, Lizbeth | López-Rincón, Omar | Rojas-Velázquez, David | Valencia-Rosado, Luis Oswaldo | Rosas-Romero, Roberto | Etcheverry, Gibran

Article Type: Research Article

Abstract: Newborn cry features extraction for affections detection and classification has been intensively developed during the last ten to fifteen years. In this work, methods from the system identification area have been implemented in order to obtain ten Linear Predictive Coefficients (LPCs) plus a nonlinear one stated as Bilinear Intermittent Factor (BIF) per 20 ms analysis window for 40 normal and loss hearing (deaf) newborn cries each. In order to show the contribution of the nonlinear feature, a Kernel Discriminant Analysis (KDA) is performed and afterwards, two classifications tests employing Supported Vector machines (SVMs) as a standard and the Expectation Maximization (EM) …algorithm over a Mixture of Experts (ME) operation, considering the BIF as an expert or parent of the LPCs, allows to obtain a 99.84% classification. Show more

Keywords: Newborn cry classification, nonlinear features, Mixture of Experts, KDA, SVMs, EM

DOI: 10.3233/JIFS-169510

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 5, pp. 3281-3289, 2018

Authors: Flores, Jorge Garcia | Meza, Iván | Colin, Émilie | Gardent, Claire | Gangemi, Aldo | Pineda, Luis A.

Article Type: Research Article

Abstract: What if service robots could tell the story of the task they’ve just realized like a story? The aim of our work is to provide service robots with natural language capabilities to produce a Robot Experience Story for its human interlocutors. Robxp stories are narratives composed of the robot’s holistic perception of a recently performed task: navigation, visual perceptions and action descriptions. We contribute with a narrate dialog model specifying the composition of situations necessary for a service robot to transform its task history record into a narrative knowledge representation. We provide SitLog algorithms allowing to analyze the …robot’s situation and behaviors sequence in order to generate a robxp story of the task. Both the dialogue model and the algorithms can be embedded as compositional behaviors in any other SitLog task structure. We instantiated our model into the Golem service robot framework on an experimental task. We believe Robxp stories generation could be integrated as a standard behavior for more complex service robot tasks. Show more

Keywords: Robot experience stories, SitLog, service robot, narrative generation

DOI: 10.3233/JIFS-169511

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 5, pp. 3291-3300, 2018

Authors: Pineda, Luis A. | Rodríguez, Arturo | Fuentes, Gibrán | Hernández, Noé | Reyes, Mauricio | Rascón, Caleb | Cruz, Ricardo | Vélez, Ivette | Ortega, Hernando

Article Type: Research Article

Abstract: In this paper a strategy for incorporating a flexible and reliable high-level inference module in service robots is presented. This module is a part of the robot’s cognitive architecture which coordinates perception, inference and action within the robot’s communication and interaction cycle. The present approach relies on an explicit representation of the structure of the task performed by the robot. There are three kinds of inferences that the robot can use opportunistically along the task: (1) diagnosis, (2) decision making and (3) planning; each kind can be used in specific situations of the task structure or performed in arbitrary situations …with recovery purposes when there is an interaction failure. In this latter case the three kinds of inference are performed sequentially in what we call the daily-life inference cycle . The inference cycle allows the incorporation of basic emotions in the robot’s behavior. A case study incorporating these functionalities in the robot Golem-III is presented. The paper is concluded with a reflection on the opportunistic use of inference schemes to support flexible and robust behavior, including the expression of emotions, in service robots. Show more

Keywords: Inference in service robots, robust behavior in service robots, robotics cognitive architecture, the Golem-III robot

DOI: 10.3233/JIFS-169512

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 5, pp. 3301-3311, 2018

Authors: Pérez-Espinosa, Humberto | Martínez-Miranda, Juan | Avila-George, Himer | Espinosa-Curiel, Ismael

Article Type: Research Article

Abstract: The advances in social robotics have extended the possibilities of their use in different applications and have also increased the sectors of users to which those applications can benefit. An attractive population of users is children. Recently, there has been a trend towards research in the design of interactive systems for children, as well as in the study of modeling the interaction between children and this type of systems. In this work, we present a study carried out with the objective of analyzing the affective response of children when interacting with a robot using speech-based communication. We collected data through …an experiment using a Wizard of Oz scenario where we induced different affective reactions in the participants. Two type of data were collected and analyzed: 1) a set of evaluators manually created annotations of emotions and attitudes to determine the distribution of emotions during the experiments and evaluate how difficult is the training of automatic classifiers to discriminate different affective states from the acoustic properties of the children’s voices; 2) we used the children’s responses from a self-evaluation questionnaire about their perceptions and preferences towards the robots, modeled with different personalities, to assess whether there are relevant differences according to their different age’s range. We obtained a large children’s speech database that would be a valuable resource for the study of paralinguistic and interaction aspects. Despite the imbalance of the database, we were able to obtain good results for the classification of emotions and attitudes. We also find some relevant differences in how young and older children note the differences in the behaviors of the robots according to the modeled personality. Differences based on children’s age were also found in the preferences towards the two different robots. Show more

Keywords: Children speech analysis, paralinguistic information, emotion recognition, social robots

DOI: 10.3233/JIFS-169513

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 5, pp. 3313-3324, 2018

Authors: González-Hernández, Francisco | Zatarain-Cabada, Ramon | Barrón-Estrada, Maria Lucia | Rodríguez-Rangel, Hector

Article Type: Research Article

Abstract: This work presents the application of a convolutional neural network (CNN) used to identify emotions through taken images to students, which are learning Java language with an Intelligent Learning Environment. The CNN contains three convolutional layers, three max-pooling layers, and three neural networks with intermediate dropout connections. The CNN was trained using different emotional databases. One of them was a posed database (RaFD) and two of them were spontaneous databases created specially by us with a content focused on learning-centered emotions. The results show a comparison among three emotion recognition systems. One applying a local binary pattern approach with facial …patches, another applying a geometry-based method, and the last one applying the convolutional network. The analysis presented satisfactory results; the CNN obtained a 95% accuracy for the RaFD database, an 88% accuracy for a learning-centered emotion database and a 74% accuracy for a second learning-centered emotion database. Results are compared against the classifiers support vector machine, k-nearest neighbors, and artificial neural network. Show more

Keywords: Convolutional neural network, educational emotion recognition, face expression database, machine learning, feature extraction

DOI: 10.3233/JIFS-169514

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 5, pp. 3325-3336, 2018

Authors: Garcia-Lopez, Francisco Javier | Batyrshin, Ildar | Gelbukh, Alexander

Article Type: Research Article

Abstract: In this paper we measure the relationship between messages in the social media and the stock market prices. First, we measure the correlation and association between the amount of stock related tweets and different financial indicators such as prices, returns and transaction volume. Then, we analyze the content of the messages and test whether the tweets generated during different trends of price change (up, down or steady) can be distinguished by automatic classifiers. Our corpus consist on messages related to nine IT companies and also their daily prices and volume during trading hours for over a period of three months. …Two textual representations were used, bag of words and word embeddings. The tweets were automatically tagged using two thresholds to bin the changes in price. We have found a correlation between the amount of daily messages and the volume of financial transactions. We also found negative association (more specifically, what we define as local trend association) between tweet volume and financial indicators that were not found by using only the correlation analysis. Our main contribution is that the messages generated during a positive, negative and neutral trend can be distinguished by state of the art classifiers. Show more

Keywords: Stock market, twitter, machine learning, bag of words, word embeddings

DOI: 10.3233/JIFS-169515

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 5, pp. 3337-3347, 2018

Authors: Ramírez-Chacón, Miguel | Hidalgo-Silva, Hugo | Chávez, Edgar

Article Type: Research Article

Abstract: Many multimedia objects accept an abstract representation as point sets, or point clouds , in the plane. Searching for objects in a collection is traduced to searching for matching point clouds. In this paper algorithms and data structures are given for indexing and searching point clouds. The indexes are implemented using off-the-shelf, popular, software components. Experimental tests were performed on large databases, including a synthetic database of 10 million point clouds (1000 points per cloud) and the MIR Flickr-1M database, which contains 1 million high-resolution images. The performance of the proposed indexes was evaluated according to: Average search time, construction …time, recall@k, memory usage and performance under insertions and deletions. A thorough comparison was performed between the fastest method available in the literature and a repertoire of implementations. The most competitive index is three orders of magnitude faster than the state of the art, in the image database, with recall  @ 1 ≥0.989 for 20% insertions and deletions. Show more

Keywords: Point cloud search, similarity search index, multimedia indexing

DOI: 10.3233/JIFS-169516

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 5, pp. 3349-3358, 2018

Authors: Arana-Llanes, Julia Y. | González-Serna, Gabriel | Pineda-Tapia, Rodrigo | Olivares-Peregrino, Víctor | Ricarte-Trives, Jorge J. | Latorre-Postigo, José M.

Article Type: Research Article

Abstract: The Computer Science department at Tecnológico Nacional de México-CENIDET, Mexico, with the collaboration of the Psychological department of the University of Castilla-La Mancha (UCLM), Spain, is on a developing process for the creation of an immersive virtual environment through virtual reality (VR) for the e-learning educational area. Such environment, works through electroencephalographic lectures (EEG) from the students, acquired by a Brain-Computer Interface (BCI), to adapt the virtual content to the profile and needs of the student on real-time basis. This system can detect the accuracy of attention and concentration levels on mental states, for the optimum development of the activities …requested on an e-learning platform; if the student is not on a suitable concentration level, the system is able to induce the student to the requested mental state. The present document shows the proposal of different recommended activities that induce the mentioned mental states and the EEG response of each one. As well, the definition of the ideal learning emotional state that will be included as a part of the future works. It is important to mention that such activities are based on psychological researches that are dedicated to measure the levels of attention, concentration and other executive functions. Show more

Keywords: EEG, attention, concentration, E-learning, human computer interaction (HCI), brain-computer interface (BCI), augmented cognition (AugCog)

DOI: 10.3233/JIFS-169517

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 5, pp. 3359-3371, 2018

Authors: Limón, Yensen | Bárcenas, Everardo | Benítez-Guerrero, Edgard | Molero, Guillermo

Article Type: Research Article

Abstract: Context-aware systems are ubiquitous computing systems capable to adapt themselves to a dynamically changing environment. Ensuring consistency in context-aware systems has proved a challenging task due to the inherent expressive power required to model dynamical systems. In the current work, we propose the use of the μ -calculus with converse, an expressive modal logic, for modeling and verifying consistency. In particular, we propose a consistency model for a context-aware communication system. Consistency is tested in terms of the satisfiability of a μ -calculus formula. We show this consistency verification method is correct and a complexity analysis is provided. We also …describe an implementation with several experiments. Show more

Keywords: Context-aware systems, automated reasoning, modal logics

DOI: 10.3233/JIFS-169518

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 5, pp. 3373-3383, 2018

Authors: Tiwari, Anoop Kumar | Shreevastava, Shivam | Shukla, K.K. | Subbiah, Karthikeyan

Article Type: Research Article

Abstract: Technological advancement in the area of computing has led to production of huge amount of structured as well as unstructured data. This high dimensional data is very complex to process. Feature selection is one of the widely used techniques for preprocessing of this huge data in predictive analytics. Rough set based feature selection is an approach for handling the vagueness in data and works fine on discrete data but struggles in the continuous case as it requires discretization. This process of discretization leads to information loss. Solution for this problem was given by various authors in form of fuzzy rough …set as well as intuitionistic fuzzy rough set based approaches for feature selection. Intuitionistic fuzzy set has certain benefits over the theory of traditional fuzzy sets such as its ability in a better expression of underlying information as well as its aptness to recite fragile ambiguities of the uncertainty of the objective world. The benefits offered by Intuitionistic fuzzy sets is due to the concurrent contemplation of positive, negative and hesitancy degrees for an object to belong to a set. In this paper, three novel approaches of feature reduction based on intuitionistic fuzzy rough set are presented. For this, a new intuitionistic fuzzy rough set model is established by defining a pair of lower and upper approximations. Furthermore, three new approaches of feature selection based on the degree of dependency by using score function, membership grade and cardinality of intuitionistic fuzzy numbers are introduced. Moreover, the basic results on lower and upper approximations based on rough sets are extended for intuitionistic fuzzy rough sets and analogous results are established. Moreover, a suitable algorithm is given based on our proposed approaches. Finally, the proposed algorithm is applied to an arbitrary example data set and comparison has been made with the previous fuzzy rough set based technique. The proposed algorithm is found to be better performing in terms of selected features. Show more

Keywords: Rough set, fuzzy-rough set, intuitionistic fuzzy-rough set, score function, degree of dependency, T-equivalence relation

DOI: 10.3233/JIFS-169519

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 5, pp. 3385-3394, 2018