Journal of Intelligent & Fuzzy Systems - Volume 42, issue 5

Authors: Gallardo-García, Rafael | Beltrán-Martínez, Beatriz | Hernández-Gracidas, Carlos | Vilariño-Ayala, Darnes

Article Type: Research Article

Abstract: Current State-of-the-Art image captioning systems that can read and integrate read text into the generated descriptions need high processing power and memory usage, which limits the sustainability and usability of the models (as they require expensive and very specialized hardware). The present work introduces two alternative versions (L-M4C and L-CNMT) of top architectures (on the TextCaps challenge), which were mainly adapted to achieve near-State-of-The-Art performance while being memory-lighter when compared to the original architectures, this is mainly achieved by using distilled or smaller pre-trained models on the text-and-OCR embedding modules. On the one hand, a distilled version of BERT was …used in order to reduce the size of the text-embedding module (the distilled model has 59% fewer parameters), on the other hand, the OCR context processor on both architectures was replaced by Global Vectors (GloVe), instead of using FastText pre-trained vectors, this can reduce the memory used by the OCR-embedding module up to a 94% . Two of the three models presented in this work surpassed the baseline (M4C-Captioner) of the challenge on the evaluation and test sets, also, our best lighter architecture reached a CIDEr score of 88.24 on the test set, which is 7.25 points above the baseline model. Show more

Keywords: M4C-Captioner, MMF, multimodal transformers, reading comprehension, TextCaps challenge

DOI: 10.3233/JIFS-219230

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 5, pp. 4399-4410, 2022

Authors: Lopez-Rincon, Omar | Starostenko, Oleg | Lopez-Rincon, Alejandro

Article Type: Research Article

Abstract: Algorithmic music composition has recently become an area of prestigious research in projects such as Google’s Magenta, Aiva, and Sony’s CSL Lab aiming to increase the composers’ tools for creativity. There are advances in systems for music feature extraction and generation of harmonies with short-time and long-time patterns of music style, genre, and motif. However, there are still challenges in the creation of poly-instrumental and polyphonic music, pieces become repetitive and sometimes these systems copy the original files. The main contribution of this paper is related to the improvement of generating new non-plagiary harmonic developments constructed from the symbolic abstraction …from MIDI music non-labeled data with controlled selection of rhythmic features based on evolutionary techniques. Particularly, a novel approach for generating new music compositions by replacing existing harmony descriptors in a MIDI file with new harmonic features from another MIDI file selected by a genetic algorithm. This allows combining newly created harmony with a rhythm of another composition guaranteeing the adjustment of a new music piece to a distinctive genre with regularity and consistency. The performance of the proposed approach has been assessed using artificial intelligent computational tests, which assure goodness of the extracted features and shows its quality and competitiveness. Show more

Keywords: Automatic music composition, music feature extraction and encoding, genetic algorithm, harmony recombination

DOI: 10.3233/JIFS-219231

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 5, pp. 4411-4423, 2022

Authors: Tandon, Kushagri | Chatterjee, Niladri

Article Type: Research Article

Abstract: Multi-label text classification aims at assigning more than one class to a given text document, which makes the task more ambiguous and challenging at the same time. The ambiguities come from the fact that often several labels in the prescribed label set are semantically close to each other, making clear demarcation between them difficult. As a consequence, any Machine Learning based approach for developing multi-label classification scheme needs to define its feature space by choosing features beyond linguistic or semi-linguistic features, so that the semantic closeness between the labels is also taken into account. The present work describes a scheme …of feature extraction where the training document set and the prescribed label set are intertwined in a novel way to capture the ambiguity in a meaningful way. In particular, experiments were conducted using Topic Modeling and Fuzzy C-Means clustering which aim at measuring the underlying uncertainty using probability and membership based measures, respectively. Several Nonparametric hypothesis tests establish the effectiveness of the features obtained through Fuzzy C-Means clustering in multi-label classification. A new algorithm has been proposed for training the system for multi-label classification using the above set of features. Show more

Keywords: Multi-label classification, clustering, fuzzy membership, topic modeling, document embeddings

DOI: 10.3233/JIFS-219232

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 5, pp. 4425-4436, 2022

Authors: Balouchzahi, Fazlourrahman | Sidorov, Grigori | Shashirekha, Hosahalli Lakshmaiah

Article Type: Research Article

Abstract: Complex learning approaches along with complicated and expensive features are not always the best or the only solution for Natural Language Processing (NLP) tasks. Despite huge progress and advancements in learning approaches such as Deep Learning (DL) and Transfer Learning (TL), there are many NLP tasks such as Text Classification (TC), for which basic Machine Learning (ML) classifiers perform superior to DL or TL approaches. Added to this, an efficient feature engineering step can significantly improve the performance of ML based systems. To check the efficacy of ML based systems and feature engineering on TC, this paper explores char, character …sequences, syllables, word n-grams as well as syntactic n-grams as features and SHapley Additive exPlanations (SHAP) values to select the important features from the collection of extracted features. Voting Classifiers (VC) with soft and hard voting of four ML classifiers, namely: Support Vector Machine (SVM) with Linear and Radial Basis Function (RBF) kernel, Logistic Regression (LR), and Random Forest (RF) was trained and evaluated on Fake News Spreaders Profiling (FNSP) shared task dataset in PAN 2020. This shared task consists of profiling fake news spreaders in English and Spanish languages. The proposed models exhibited an average accuracy of 0.785 for both languages in this shared task and outperformed the best models submitted to this task. Show more

Keywords: Fake news, Learning approaches, N-grams, Feature engineering, SHAP values

DOI: 10.3233/JIFS-219233

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 5, pp. 4437-4448, 2022

Authors: Suárez-Cansino, Joel | López-Morales, Virgilio | Ramos-Fernández, Julio César

Article Type: Research Article

Abstract: Building a good instructional design requires a sound organization management to program and articulate several tasks based for instance on the time availability, process follow-up, social and educational context. Furthermore, learning outcomes are the basis involving every educational activity. Thus, based on a predefined ontology, including the instructional educative model and its characteristics, we propose the use of a Long Short–Term Memory Artificial Neural Network (LSTM) to organize the structure and automatize the obtention of learning outcomes for a focused instructional design. We present encouraging results in this direction through the use of a LSTM using as the training data, …a small learning outcomes set predefined by the user, focused on the characteristics of an educative model previously defined. Show more

Keywords: Long short–term memory artificial neural network, educative model, instructional design, automatic learning outcomes, ontology

DOI: 10.3233/JIFS-219234

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 5, pp. 4449-4461, 2022

Authors: Sánchez-Fernández, Manuel-Alejandro | Medina-Urrea, Alfonso | Torres-Moreno, Juan-Manuel

Article Type: Research Article

Abstract: The present work aims to study the relationship between measures, obtained from Latent Semantic Analysis (LSA) and a variant known as SPAN , and activation and identifiability states (Informative States) of referents in noun phrases present in journalistic notes from Northwestern Mexican news outlets written in Spanish. The aim and challenge is to find a strategy to achieve labelling of new / given information in the discourse rooted in a theoretically linguistic stance. The new / given distinction can be defined from different perspectives in which it varies what linguistic forms are taken into account. Thus, the focus in this …work is to work with full referential devices (n  = 2 388). Pearson’s R correlation tests, analysis of variance, graphical exploration of the clustering of labels, and a classification experiment with random forests are performed. For the experiment, two groups were used: noun phrases labeled with all 10 tags of informative states and a binary labelling, as well as the use of two bags-of-words for each noun phrase: the interior and the exterior. It was found that using LSA in conjunction with the inner bag of words can be used to classify certain informational states. This same measure showed good results for the binary division, detecting which sentences introduce new referents in discourse. In previous work using a similar method in noun phrases in English, 80% accuracy (n  = 478) was reached in their classification exercise. Our best test for Spanish reached 79%. No work on Spanish using this method has been done before and this kind of experiment is important because Spanish exhibits a more complex inflectional morphology. Show more

Keywords: Automatic tagging, activation states, latent semantic analysis, noun phrases, computational pragmatics

DOI: 10.3233/JIFS-219235

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 5, pp. 4463-4471, 2022

Authors: Sierra, Gerardo | Hernández-García, Tonatiuh | Gómez-Adorno, Helena | Bel-Enguix, Gemma

Article Type: Research Article

Abstract: In this paper, we present authorship attribution methods applied to ¡El Mondrigo! (1968), a controversial text supposedly created by order of the Mexican Government to defame a student strike. Up to now, although the authorship of the book has been attributed to several journalists and writers, it could not be demonstrated and remains an open problem. The work aims at establishing which one of the most commonly attributed writers is the real author. To do that, we implement methods based on stylometric features using textual distance, supervised, and unsupervised learning. The distance-based methods implemented in this work are Kilgarriff and …Delta of Burrows, an SVM algorithm is used as the supervised method, and the k -means algorithm as the unsupervised algorithm. The applied methods were consistent by pointing out a single author as the most likely one. Show more

Keywords: Machine learning, stylometry, authorship attribution

DOI: 10.3233/JIFS-219236

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 5, pp. 4473-4480, 2022

Authors: García-Gorrostieta, Jesús Miguel | López-López, Aurelio | González-López, Samuel | López-Monroy, Adrián Pastor

Article Type: Research Article

Abstract: Academic theses writing is a complex task that requires the author to be skilled in argumentation. The goal of the academic author is to communicate clear ideas and to convince the reader of the presented claims. However, few students are good arguers, and this is a skill that takes time to master. In this paper, we present an exploration of lexical features used to model automatic detection of argumentative paragraphs using machine learning techniques. We present a novel proposal, which combines the information in the complete paragraph with the detection of argumentative segments in order to achieve improved results for …the detection of argumentative paragraphs. We propose two approaches; a more descriptive one, which uses the decision tree classifier with indicators and lexical features; and another more efficient, which uses an SVM classifier with lexical features and a Document Occurrence Representation (DOR). Both approaches consider the detection of argumentative segments to ensure that a paragraph detected as argumentative has indeed segments with argumentation. We achieved encouraging results for both approaches. Show more

Keywords: Academic writing, argumentation analysis, machine learning, text representation, natural language processing

DOI: 10.3233/JIFS-219237

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 5, pp. 4481-4491, 2022

Authors: Ruiz Alonso, Dorian | Zepeda Cortés, Claudia | Castillo Zacatelco, Hilda | Carballido Carranza, José Luis

Article Type: Research Article

Abstract: In this work, we propose the extension of a methodology for the multi-label classification of feedback according to the Hattie and Timperley feedback model, incorporating a hyperparameter tuning stage. It is analyzed whether the incorporation of the hyperparameter tuning stage prior to the execution of the algorithms support vector machines, random forest and multi-label k-nearest neighbors, improves the performance metrics of multi-label classifiers that automatically locate the feedback generated by a teacher to the activities sent by students in online courses on the Blackboard platform at the task, process, regulation, praise and other levels proposed in the feedback model by …Hattie and Timperley. The grid search strategy is used to refine the hyperparameters of each algorithm. The results show that the adjustment of the hyperparameters improves the performance metrics for the data set used. Show more

Keywords: Text mining, multi-label classification, hyperparameter tuning, online courses

DOI: 10.3233/JIFS-219238

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 5, pp. 4493-4501, 2022

Authors: Medina Nieto, María Auxilio | de la Calleja Mora, Jorge | Zepeda Cortés, Claudia | López Domínguez, Eduardo

Article Type: Research Article

Abstract: This paper describes Onto4AIR2, an ontology to manage theses from open repositories, this fosters unique and formal definitions of concepts from the Mexican repositories domain in English and Spanish languages, its goal is to support the construction of machine-readable datasets that are semantically labeled for further consultations in educational organizations. The ontology instances are sample data of theses from the National Repository of Mexico, an initiative promoted by the National Council of Science and Technology. The paper describes advantages derived from the formalisms of the ontology, and describes an assessment technique where participants are developers and potential users. Developers followed …a competency questions-based approach and determined that the ontology represents questions and answers using its terminology; whereas potential users participated in a satisfaction survey; the results showed a positive perception. At present, the level of the ontology is proof of concept. Show more

Keywords: Semantic web, ontologies, machine readable datasets, open data repositories, metadata management

DOI: 10.3233/JIFS-219239

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 5, pp. 4503-4512, 2022